Publications

Innovative testing and validation methods are prerequisites concerning Connected, Cooperative, and Automated Mobility (CCAM), as the high number of cooperating participants and concurrent processes critically increase the probability of adverse safety and security incidents. The proposed new approaches deal with this increasing complexity of not currently having generally accepted validation mechanisms. The paper introduces a novel, mathematical model based, scenario identification methodology, facilitating the selection of critical road vehicle traffic scenarios, taking into account different testing objectives, such as maximizing the safety risk of the analyzed system. The presented results verify that applying specific decision models and quantifiable indicators related to the system elements of highly automated mobility systems can significantly contribute to the systematic identification of unsafe corner cases in connected and cooperative autonomous systems.

Our paper introduces new reconstruction techniques of real-life critical road traffic accidents focusing on highly automated functions. The investigation method presented here focuses on the effect of relevant control parameters and environmental factors following the concept of sensitivity analysis. Two reconstruction tools are applied, the choice depending on the relevant causal factor of the accidents. Our measurement proves that the technical parameters of the control process, like time to collision or braking pressure that affects user satisfaction directly, can significantly influence the probability of accident occurrence. Thus, it is reasonable to consider safety with an increased weight compared to the user experience when identifying these parameters’ values. On the other hand, the effects of the investigated environmental factors were also found to be significant. Accordingly, future ADAS applications need to consider the change of environmental factors in the case of increased risk level, and driver-mode should be adapted to the new situation.

In the future, the role of the human factor in the driving processes is expected to decrease continuously. At the same time, based on the global trends, the role of computer-supported decision systems and artificial intelligence (AI)-based control solutions increases in relation to driving processes, which carries a significant safety-enhancing potential. To assess the possible social benefits of automated vehicle systems objectively, it is necessary to analyze the possible negative effects in detail as well. Accordingly, the aim of this article is to present a statistical survey of crashes involving automated vehicles today in order to identify and evaluate the factors that are relevant in the crashes. The analyzed data showed that when the autopilot mode was turned off and the human driver made the control decisions, the severity of crashes on straight roads was greater at α = 0.1 significance level than when the vehicle was in autopilot mode and the vehicle system made the control decisions. In addition, if the α significance level is 0.2, then crashes on plain terrain, during the day, or in the speed range of 80-100 km/h are generally less serious for vehicles driven in autopilot mode than for vehicles with autopilot mode turned off. In light of the considerations above, it is also important to emphasize that this paper only investigates crash severity given occurrence but not the probability of occurrence itself.

The representation of objects in LiDAR point clouds is changed as the height of the mounting position of sensor devices gets increased. Most of the available open datasets for training machine learning based object detectors are generated with vehicle top mounted sensors, thus the detectors trained on such datasets perform weaker when the sensor is observing the scene from a significantly higher viewpoint (e.g. infrastructure sensor). In this paper a novel Automatic Label Injection method is proposed to label the objects in the point cloud of the high-mounted infrastructure LiDAR sensor based on the output of a well performing “trainer” detector deployed at optimal height while considering the uncertainties caused by various factors described in detail throughout the paper. The proposed automatic labeling approach has been validated on a small scale sensor setup in a real-world traffic scenario where accurate differential GNSS reference data where also available for each test vehicle. Furthermore, the concept of a distributed multi-sensor system covering a larger area aimed for automatic dataset generation is also presented. It is shown that a machine learning based detector trained on differential GNSS-based training dataset performs very similarly to the detector retrained on a dataset generated by the proposed Automatic Label Injection technique. According to our results a significant increase in the maximum detection range can be achieved by retraining the detector on viewpoint specific data generated fully automatically by the proposed label injection technique compared to a detector trained on vehicle top mounted sensor data.

In this paper, a linear time-varying model predictive controller (LTV-MPC) is proposed for automated vehicle path-following applications. In the field of path following, the application of nonlinear MPCs is becoming more common; however, the major disadvantage of this algorithm is the high computational cost. During this research, the authors propose two methods to reduce the nonlinear terms: one is a novel method to define the path-following problem by transforming the path according to the actual state of the vehicle, while the other one is the application of a successive linearization technique to generate the state–space representation of the vehicle used for state prediction by the MPC. Furthermore, the dynamic effect of the steering system is examined as well by modeling the steering dynamics with a first-order lag. Using the proposed method, the necessary segment of the predefined path is transformed, the linearized model of the vehicle is calculated, and the optimal steering control vector is calculated for a finite horizon at every timestep. The longitudinal dynamics of the vehicle are controlled separately from the lateral dynamics by a PI cruise controller. The performance of the controller is evaluated and the effect of the steering model is examined as well.

Future transportation is expected to be connected, cooperative, and highly automated. Classic automated vehicular functions are developed based on safety principles primarily, but the reliability and cybersecurity of wireless communication processes pose new challenges for the automotive industry for connected vehicles, representing Connected, Cooperative and Automated Mobility (CCAM) systems as cyber-physical systems. Since CCAM system safety is significantly affected by network performance metrics, we need to consider communication factors such as end-to-end latency and packet delivery ratio during the safety evaluation of highly automated vehicle functions. This study presents a methodological framework which links the safety of the CCAM systems with the cyber security sensitive network performance metrics, as well as the vehicle dynamics factors in order to evaluate the effect of intentional or unintentional communication failures. Accordingly, we introduced a new approach to characterize the safety risk of specific Vehicle-to-Vehicle (V2V) applications. Beyond this, severity of collision and message reception probability functions were introduced to further investigate the safety of the analyzed application. Finally, the values of the safety risk index for different warning intervals are summed, which suggest that the safety effect of specific function-related parameters could be and shall be represented during the function development process.

Vehicle dynamics models are widely used in many areas of the automotive industry. The usability of each model depends on how well it is able to mimic the behavior of the real vehicle. Each simulation model must go through a thorough investigation process first, which is called model validation. Although, vehicle dynamics simulation models and methodology for computational model validation are well established fields, to the best of the authors' knowledge a general framework for vehicle dynamics model validation is still lacking. The research aims to develop a comprehensive methodological framework for vehicle dynamics model validation. In this paper the aim is to present the high level layout of the proposed framework, introducing the main blocks and the tasks related, also addressing some critical issues regarding vehicle dynamics model validation such as validation metrics and vehicle parameter measurement and estimation. An important part of the proposed methodology is a sophisticated vehicle dynamics measurement system, which gives the opportunity to estimate a bunch of vehicle parameters during dynamic testing, which can be useful for several reasons, e.g. fine-tuning the parameters of the Pacejka Magic formula. As a case study some vehicle dynamics test based parameter estimations are shown to justify the raison d'être and investigate possible applications. INDEX TERMS Vehicle model validation, vehicle dynamics, vehicle parameter identification, methodological framework.

There is consensus in industry and academia that Highly Automated Vehicles (HAV) and Connected Automated Vehicles (CAV) will be launched into the market in the near future due to emerging autonomous driving technology. In this paper, a mixed traffic simulation framework that integrates vehicle models with different automated driving systems in the microscopic traffic simulation was proposed. Currently, some of the more mature Automated Driving Systems (ADS) functions (e.g., Adaptive Cruise Control (ACC), Lane Keeping Assistant (LKA), etc.) are already equipped in vehicles, the very next step towards a higher automated driving is represented by Level 3 vehicles and CAV which show great promise in helping to avoid crashes, ease traffic congestion, and improve the environment. Therefore, to better predict and simulate the driving behavior of automated vehicles on the motorway scenario, a virtual test framework is proposed which includes the Highway Chauffeur (HWC) and Vehicle-to-Vehicle (V2V) communication function. These functions are implemented as an external driver model in PTV Vissim. The framework uses a detailed digital twin based on the M86 road network located in southwestern Hungary, which was constructed for autonomous driving tests. With this framework, the effect of the proposed vehicle models is evaluated with the microscopic traffic simulator PTV Vissim. A case study of the different penetration rates of HAV and CAV was performed on the M86 motorway. Preliminary results presented in this paper demonstrated that introducing HAV and CAV to the current network individually will cause negative effects on traffic performance. However, a certain ratio of mixed traffic, 60% CAV and 40% Human Driver Vehicles (HDV), could reduce this negative impact. The simulation results also show that high penetration CAV has fine driving stability and less travel delay.

The paper introduces a novel approach to the classical adaptive traffic signal control (TSC) problem. Instead of the traditional optimization or simple rule-based approach, Artificial Intelligence is applied. Reinforcement Learning is a spectacularly evolving realm of Machine Learning which owns the key features such as generalization, scalability, real-time applicability for solving the traffic signal control problem. Nevertheless, the researchers’ responsibilities become more serious regarding the formulation of state representation and the rewarding system. These Reinforcement Learning features are also the most fascinating and controversial virtues since the utilized abstractions decide whether the algorithm solves the problem or not. This paper proposes a new interpretation for the feature-based state representation and rewarding concept that makes the TSC problem highly generalizable and has scaling potential. The proposed method’s feasibility is demonstrated via a simulation study using a high-fidelity microscopic traffic simulator. The results justify that the Deep Reinforcement Learning based approach is a real candidate for real-time traffic light control.

In this article, the basic Reinforcement Learning (RL) concepts are discussed, continued with a brief explanation of Markov Decision Processes (MDPs). Reasoning for the application of RL in the autonomous vehicle control domain is accompanied with a developed basic environment for simulation-based training of agents. Furthermore, we look at the available literature of successor features, and the recent achievements of its utilization. Our motivation is to tackle the problem of credit assignment with reward decomposition by using successor features, because the complex tasks while driving can cause unsuccessful training and can be challenging. After explaining the applied methodology and showing how it works, state-of-the-art ideas are investigated and infused into the vehicle control realm. Moreover, the paper details how these features can be tailored to the highway driving scenarios and what is the secret behind its capability to boost the performance of the RL agent. In order to investigate the proposed problems in a credible way we applied a high-fidelity traffic simulator (SUMO) as our environment, and concluded different trainings based on various scenarios. We present successor features applied to an autonomous vehicle control setting, such as highway commute. Our results imply that learned skills can help with the multi-objective rewarding problem, and agents applied to changing reward systems can adapt quickly to the new tasks. The only thing to find is the correct decomposition and selection of successor features.

Digital twins of road surfaces support multiple engineering applications. Remote sensing technologies provide information from the entire surface of the pavement by high accuracy point clouds. Pavement errors and differences from designed geometry can be detected and assessed using such datasets, while OpenCRG models derived from point clouds support transportation applications. High-resolution CRG (Curved Regular Grid) models enable analyzing vehicle suspension systems in vehicle dynamics simulation environments. Furthermore, such models also support creating the digital twins of vehicle suspensions and improve the development and research of models related to vehicle dynamics. The paper presents how the suspension digital twin was obtained applying a genetic algorithm and how it was assessed. The quality of raw data and that of the derived methods are analyzed in the case of multiple mapping technologies (terrestrial, mobile, and aerial laser scanning). CRG models were created from all datasets, and their applicability was investigated to support vehicle simulations with high accuracy demand. Other important vehicle-related use cases are also mentioned in the paper.

As advanced driver assistance systems (ADAS) are continuously upgrading, the testing standards, descriptions are evolving as well. The border disappears more and more between simulation and real-world testing. For more accurate repeatable scenarios we need a new method, which helps to make faster the process of scenario creation. The result for this would be the connection of simulated scenarios and real test equipment. Our result includes several components such as HD maps, industrial simulation software, OpenSCENARIO compatibility, Matlab programming, AB Dynamics software and test equipment. With HD maps and OpenSCENARIO compatible simulation software we can create GPS-accurate scenarios. Then, the own developed MATLAB program can convert the OpenSCENARIO file into a new format, which is readable by AB Dynamics software. After this process the last step is the real-world testing with AB Dynamics test equipment.

The testing of Connected and Automated Vehicles (CAVs) and that of the smart infrastructure (traffic control devices and vehicle sensors) in relation with CAVs will be supported by the development of a novel type of road traffic light management system at ZalaZONE Automotive Proving Ground. The system to be developed aims to allow a fully flexible traffic signal control during vehicle and system testing. The system shall provide a freely programmable open Application Programming Interface (API) towards the traffic light control units in contrast with traditional(rigid and closed) traffic control equipments. In this concept, each traffic light control unit will be made available via a remote control software running on a cloud system.

Self-driving vehicles also need maps - just like us drivers - for route plans, but traditional maps are often inaccurate and contain significantly less information than so-called High Definition Maps (HD). HD maps are usually based on laser measurements, thus the accuracy of each road element can be around 2 centimeters. Our goal is to properly support research in this direction as well, therefore we created an HD map of the selected modules of the ZalaZONEproving ground. The selected components are the High-Speed Handling course, Dynamic Platform, ADAS surface and Motorway section. These materials are accessible for download including the high-resolution and high-precision Lidar point cloud which the maps are based on, recorded with the Lecia Pegasus 2 system. We have implemented our HD maps in the increasingly widespread OpenDRIVE format in the automotive industry.

In autonomous cars accurate and reliable detection of objects in the proximity of the vehicle is necessary in order to perform further safety-critical actions which depend upon it. Many detectors have been developed in the last few years, but there is still demand for more reliable and more robust detectors. Some detectors rely on a single sensor, while some others are based upon fusion of data from multiple sources. The main aim of this paper is to show how image features can contribute to performance improvement of detectors which rely on point cloud data only. In addition it will be shown, how lidar reflectance data can be substituted by low-level image features without degrading the performance of detectors. Three different approaches are proposed to fuse image features with point cloud data. The extended networks are compared with the original network and tested on a well-known dataset and on our own data, as well. This might be important when the same pre-trained model is to be used on data generated by a lidar using different reflectance encoding schemes and when due to the lack of training data retraining is not possible. Different augmentation techniques have been proposed and tested on the KITTI dataset as well as on data acquired by a different lidar sensor. The networks augmented with image features achieved a recall increase of a few percent for occluded objects.

Automation in road public transportation has been facilitated by the emergence of infocommunication and vehicle technologies. The widely accepted definitions of automation level focus solely on the driving aspects of vehicles. However, automation covers even more fields: service planning and management, vehicle and traffic control, and passenger-handling functions. Therefore, the main outcome of this paper is a method to be applied for the handling of complex automation levels, which gives a more comprehensive assessment of new transport technologies and mobility services. Functions and function categories are all described. The paper defines four levels of automation, with a detailed description of each level and each function. The method can be applied efficiently to the analysis and comparison of services, thereby highlighting possible fields of development. Mobility services can be described by values only in a general and simplified manner. To demonstrate the applicability of the proposed method, existing services are assessed. Dial-a-bus service, taxi, ride sourcing, and car sharing are evaluated using the novel method, which provides an objective and comprehensive comparison of these services.

A spectacular measurement campaign was carried out on a real-world motorway stretch of Hungary with the participation of international industrial and academic partners. The measurement resulted in vehicle based and infrastructure based sensor data that will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles—equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization—carried out special test scenarios while collecting detailed data using different sensors. All of the test runs were recorded by both vehicles and infrastructure. The paper also showcases application examples to demonstrate the viability of the collected data having access to the ground truth labeling. This data set may support a large variety of solutions, for the test and validation of different kinds of approaches and techniques. As a complementary task, the available 5G network was monitored and tested under different radio conditions to investigate the latency results for different measurement scenarios. A part of the measured data has been shared openly, such that interested automotive and academic parties may use it for their own purposes.

Autonomous or highly automated road vehicles and all related technologies are under intensive research and development. Moreover, internationally a massive investment increase can be observed in the automotive industry. According to this megatrend, new automotive test tracks appear or older ones transform to be capable of testing and proving for autonomous vehicles. Therefore, the question emerges: what are the key areas for automated drive development, which must be financed in case of autonomous proving ground design? It is a real challenge to be able to make the right decisions due to a lack of numerous experiences in this field. In this research, experts of automated driving technology have been surveyed and their opinion and knowledge have been synthesized. As a strong purpose of gaining robust results, the conventional AHP has been amended by the Pareto approach to ensure that the derived weights correspond to the expert scoring intention so perfectly that it cannot be more improved. Since the non-Pareto optimal weight results might cause rank reversal in the final prioritization, the applied Pareto test guarantees that the final outcome reflects the expert evaluators’ incentive. The conducted analysis has indicated that the obtained results are robust not only from the sensitivity point of view but also from the Pareto optimality approach. The proposed hierarchical decision model is therefore applicable to assist decision making for autonomous proving ground developments. The main contribution of the paper, however, is to present the first reliable prioritization of the autonomous proving ground elements to extend the body of professional knowledge.

Testing self-driving vehicles is still a new and immature process; the globally harmonised procedure expected much later. The resource-demanding nature of real-world tests makes it indispensable to develop and improve the efficiency of virtual environment based testing methods. Accordingly, a novel X-in-the-Loop framework is proposed to fully exploit the recent advances in info-communication technologies, vehicle automation, and testing and validation requirements. This methodology real-time connects physical and virtual testing with high correlation while completely blurs the sharp boundaries between them. Measurement results confirm the superior performance of the 5G communication link in providing a stable, real-time connection between the real world and its virtual representation. The live demonstration proved the presented concept at the newly constructed Hungarian proving ground for automated driving. The performed investigation also includes comprehensive benchmarking, focusing on the most up-to-date automotive testing frameworks. The analysis considers the methodologies and techniques applied by the most relevant actors in the automotive testing sector worldwide. Accordingly, the newly developed testing framework is evaluated and validated in light of the state-of-the-art methods used by the automotive industry.

This article presents two prospective methods for vehicle distance estimation utilizing a single camera at different altitudes and camera pitch angles. A distance estimation procedure was conducted applying real-life visualization and homography approaches. Experimental outputs showed that the real-life visualization method overperforms the homography at all altitudes and distance ranges. The performance of the homography can be improved by locating the camera at higher locations with more pronounced tilt angles as it would provide less error / pixel at the cost of a reduced field of view. Overall, both introduced techniques perform better at higher camera positions and closer target distances. Application of mentioned methods in vehicles is a simple and economical way to improve their autonomy and reduce road risk level.

The paper describes a mixed reality environment for testing highly automated vehicle functions. The proposed Scenario-inthe-Loop test system connects real-time computer simulation with real elements being applicable at automotive proving ground. The paper outlines necessary hardware and software requirements and proposes basic system architecture. The limitation and bottleneck of the system are also identified: latency of the wireless communication constraints the accuracy of the test system. However, the presented framework can contribute to efficient development, testing and validation of automated cars. The Scenario-In-The-Loop architecture has also been justified by real-world demonstration using an experimental 5G New Radio network technology.

This documentation provides helpful information about the following topics: • Usage of OMNeT++ network simulator with Veins and INET frameworks • Implementation of the ITS Facilities Layer (simulation of standard CAM/DENM messages) in OMNeT++ • Using SUMO in multi-client mode together with OMNeT++ and Matlab/Simulink • Interfacing Matlab/Simulink with OMNeT++ using Named Shared Memory • Implementing Road Side Units in the Veins-INET project It also provides a step-by-step guide for the installation of the aforementioned software tobe able to run a co-simulation and provides example codes. In addition, some common (and undocumented) bugs and best practices are included in this documentation.

We demonstrate a working functional prototype of a cooperative perception system that maintains a real-time digital twin of the traffic environment, providing a more accurate and more reliable model than any of the participant subsystems—in this case, smart vehicles and infrastructure stations—would manage individually. The importance of such technology is that it can facilitate a spectrum of new derivative services, including cloud-assisted and cloud-controlled ADAS functions, dynamic map generation with analytics for traffic control and road infrastructure monitoring, a digital framework for operating vehicle testing grounds, logistics facilities, etc. In this paper, we constrain our discussion on the viability of the core concept and implement a system that provides a single service: the live visualization of our digital twin in a 3D simulation, which instantly and reliably matches the state of the real-world environment and showcases the advantages of real-time fusion of sensory data from various traffic participants. We envision this prototype system as part of a larger network of local information processing and integration nodes, i.e., the logically centralized digital twin is maintained in a physically distributed edge cloud.

Autonomous intersection control received particular interest recently. Theoretically, such traffic light-free controls can greatly improve the throughput of intersections with autonomous vehicles. The objective of this paper is to show how even a simple intersection control algorithm is susceptible to communication imperfections through detailed modeling of the communication layers. Safety and sensitivity analyses were carried out using a distributed control architecture, where no central control units are present. The control relies only on data contained in standard Cooperative Awareness Messages. The vehicle ad-hoc network communication is simulated with multiple layers considering many critical factors like signal propagation, noise, channel load, and even faulty units. Simulation results suggest that autonomous intersection control is robust against minor faults or communication errors. On the other hand, significant communication breakdowns lead to collisions.

The massive demand for road transportation will cause traffic congestion. This issue is especially severe when the motorway transitions to the urban road network. In view of the characteristics of the connection between urban road networks and motorway import and export, and with the aim of increasing the utilization rate of roads, alleviating road traffic congestion, and reducing motor vehicle pollutant emissions, this paper evaluated the effects of speed limit reduction in an area of Budapest, Hungary. The evaluation is based on an integrated model that combines the microscopic traffic simulation model VISSIM with the emission model COPERT. By applying the variable speed limit system, a reasonable speed limit value is formulated for the upstream road section of the congested section according to the traffic performance measured at the congested area. The results show a considerable reduction in Carbon monoxide and Nitrogen oxides emissions.

In transportation modeling, after defining a road network and its origin-destination (OD) matrix, the next important question is how to assign traffic among OD-pairs. Nowadays, advanced traveler information systems (ATIS) make it possible to realize the user equilibrium solution. Simultaneously, with the advent of the Cooperative Intelligent Transport Systems (C-ITS), it is possible to solve the traffic assignment problem in a system optimum way. As a potential traffic assignment method in the future transportation system for automated cars, the deterministic system optimum (DSO) is modeled and simulated to investigate the potential changes it may bring to the existing traditional traffic system. In this paper, stochastic user equilibrium (SUE) is used to simulate the conventional traffic assignment method. This work concluded that DSO has considerable advantages in reducing trip duration, time loss, waiting time, and departure delay under the same travel demand. What is more, the SUE traffic assignment has a more dispersed vehicle density distribution. Moreover, DSO traffic assignment helps the maximum vehicle density of each alternative path arrive almost simultaneously. Furthermore, DSO can significantly reduce or avoid the occurrence of excessive congestion.

Usage of simulation techniques like Vehicle-in-the-Loop, Scenario-in-the-Loop, and other mixed-reality systems are becoming inevitable in autonomous vehicle development, particularly in testing and validation. These methods rely on using digital twins, realistic representations of real vehicles, and traffic in a carefully rebuilt virtual world. Recreating them precisely in a virtual ecosystem requires many parameters of real vehicles to follow their properties in a simulation. This is especially true for vehicle dynamics, where these parameters have high impact on the simulation results. The paper's objective is to provide a method that can help reverse engineering a real car's suspension characteristics with the help of a genetic algorithm. A detailed description of the method is presented, guiding the reader through the whole process, including the meta-heuristic function's settings and how it interfaces with IPG Carmaker. The paper also presents multiple measurements, which can be effortlessly recreated without expensive devices or the need to disassemble any vehicle parts. Measurements are reproduced in two separate simulation tools with special scenarios providing an efficient way to analyze and verify the results. The provided method creates vehicle suspension characteristics with adequate quality, opening up the possibility to use them in the creation of digital twins or creating virtual traffic with realistic vehicle dynamics for high-quality visualization. Results show satisfying accuracy when tested with OpenCRG.

Road traffic monitoring at intersections is important in traffic engineering practice. Measured data together with traffic estimation represents base input information for traffic management or for road infrastructure development. Measuring turning rates is problematic in roundabouts due to their special geometry. This needs laborious manual traffic counts or other special methods such as automatic number-plate recognition by image processing or Bluetooth sensing, which are more costly compared to the simple automatic cross-sectional detection. As a cost-effective solution to this problem, a hybrid solution is suggested, i.e. using cross-sectional detection combined with advanced estimation. The paper investigated different methods. Traditional iteration based approach as well as estimators adopted from control theory were benchmarked and validated on real-world traffic data as well as via microscopic traffic simulation. Considering different error metrics, it is shown that constrained Kalman Filter and Moving Horizon Estimation provide reliable solution to the turning rate estimation problem in roundabouts.

Road traffic congestion has become an everyday phenomenon in today's cities all around the world. The reason is clear: at peak hours, the road network operates at full capacity. In this way, growing traffic demand cannot be satisfied, not even with traffic-responsive signal plans. The external impacts of traffic congestion come with a serious socio-economic cost: air pollution, increased travel times and fuel consumption, stress, as well as higher risk of accidents. To tackle these problems, a number of European cities have implemented reduced speed limit measures. Similarly, a general urban speed limit measure is in preparatory phase in Budapest, Hungary. In this context, a complex preliminary impact assessment is needed using a simulated environment. Two typical network parts of Budapest were analyzed with microscopic traffic simulations. The results revealed that speed limits can affect traffic differently in diverse network types indicating that thorough examination and preparation works are needed prior to the introduction of speed limit reduction.

Considering the recent advances in autonomous driving technology, conventional on-road based testing tools cannot meet the validation requirements with respect to time and cost-efficiency. Scenario-based virtual validation methods offer an efficient virtual testing approach that supports the development and contribute to decreased on-road testing. X-in-the loop testing methods contribute to a stepwise increase in test/validation quality by introducing hardware/software elements starting from the component up to full vehicle level and have been increasingly integrated into the process of design and validation of intelligent vehicle systems. In this paper, a novel framework that includes a highly dynamic vehicle-in-the-loop testbed, a traffic flow simulation method is introduced to precisely assess the impact of specific variables on the performance of an intelligent system with all vehicle components included.

This study aims to provide a new approach for describing and measuring the vulnerability of in-vehicle networks regarding cyberattacks. Cyberattacks targeting in-vehicle networks can result in a reasonable threat considering passenger safety. Unlike previous literature, the methodology focuses on a comparatively large sample of vehicle networks (114 objects) by proposing a new framework of statistical techniques for measuring, classifying, and modelling in-vehicle networks concerning the changed vulnerability, instead of dealing with each vehicle network individually. To facilitate understanding of the vulnerability patterns of in-vehicle networks, the dataset has been evaluated through three analytic stages: vulnerability identification, classification, and modeling. The result has helped in ranking vehicles based on their network vulnerability level. The result of the modeling has shown that every additional remote endpoint installation causes a relevant weakening in security. Higher cost vehicles have also appeared to be more vulnerable to cyberattacks, while the increase in the number of segmented network domains has had a positive effect on network security.

Road traffic signals and traffic infrastructure have a significant impact on the behaviour of highly automated or autonomous vehicles. However, the increase in automation does not always mean an advantage. Generally, highly automated vehicles strictly follow the traffic rules resulting in near-accident situations, although their goal is to avoid and reduce them. Malfunctions of the automated functions might cause surprising interventions while the vehicle is in motion, drivers cannot react in time and well. This paper highlights the potential danger and uncertainty of highly automated or autonomous vehicles in the context of the current conventional traffic infrastructure system. In the future, special consideration shall be given to the vehicle industry and traffic regulation makers on how the infrastructure should be adapted to automated vehicle functions to have a seamless shift towards automated driving. The paper sums up many problematic situations with two critical problems with sensitivity analysis. The first situation: the speed assist system (based on speed limit sign recognition) conflicts with the traffic infrastructure. The second situation is shown: the ACC (Adaptive Cruise Control) and LKA (Lane Assist) contradicts with the traffic infrastructure. These critical situations were investigated by using an high-fidelity automotive simulation software as proof of concept and were examined by accident reconstruction analysis software.

The spread of connected vehicles is expected to multiply the effects of the growing penetration of cyberspace in our life, and with this it remarkably influences the vulnerability of society to cyberattacks in an unfavorable way. Accordingly, ZalaZONE - the Hungarian test track - has set up a working group to support the necessary methodological background for cybersecurity-related validation processes for the automotive industry. Therefore the paper aims to reconsider safety integrity levels in the automotive industry related to the field of cybersecurity. Following this, the article provides a comprehensive structure of integrity levels that serves the safety requirements of nowadays new cybersecurity challenges. To adapt the new cybersecurity integrity level architecture to the conventional automotive safety integrity level framework, the authors have verified a specific clustering model. To validate the aggregated probability of the possible cyberattack alternatives, the generated new cybersecurity rating scale is compared with the ASIL probability values by applying the Kolmogorov-Smirnov test. In the final step of the analysis, the possible practical application of the new framework is presented based on the identification of the derived probability value in the case of the investigated failure mode.

Since modern vehicles are connected and their transport processes are strongly supported by different automated functions, malicious external interventions can impair safety integrity. Therefore, it seems to be reasonable in the future to introduce safety and security co-engineering approaches in the automotive industry. With regard to the performed evaluation, three main promising research orientations have been identified. Automotive safety and security related development of co-engineering methodology and validation framework are of key importance from the viewpoint of autonomous transportation. Accordingly, a scenario based, integrated evaluation of automotive safety and security would be closely fit to the concept of SOTIF and the SoS approach. Beyond this, the communication and network security of "vehicle to everything" channels have to also be in the focus of automotive researches. Additionally, the development of automotive anomaly detection systems, especially focusing on the complex SoS operation processes will be a highly important research orientation.

Developing a vehicle is always a long and complex task. This is especially true for autonomous cars. Tasks performed by the driver are taken over by the vehicle and must be performed with maximum reliability. Developing these systems is a difficult task, especially due to limited testing capabilities. Testing a vehicle in a closed environment is safe and controlled, but the variety of test scenarios is limited. By using mixed reality environments, one can create a diverse environment around a real test vehicle, with traffic, obstacles, and unexpected situations. The real movement of the test vehicle allows testing decision and motion planning level vehicular functions. Information from the virtual world can be considered as input to the vehicle sensor. Mixed reality or digital twin simulation environments can greatly assist the autonomous vehicle development process and also serve as a basis for validation procedures for such systems.This article introduces a mixed reality simulation environment that integrates a real test vehicle into a virtual environment, can handle other real and virtual obstacles, contains traffic simulations, and visualizes all of these.

In recent years vehicles with high level of automation are becoming more popular which is expected to continue in the future. Advanced driving assistance systems are increasingly taking control of the vehicle, starting with the support of the driving task. Automated or highly automated vehicles are expected to follow a planned road safely. In this paper, the authors aim to improve the accuracy of path following by developing a new control strategy. The controller includes both pure pursuit and Stanley methods, the operation of the controller is based on the geometry of the vehicle and the path; the key is the proper weighting between two controllers. The performance of the combined path following controller was measured on a demonstration vehicle.

Under proofread The traditional automotive homologation processes aim to ensure the safety of vehicles on public roads. Autonomous vehicles (AV) with artificial intelligence (AI) are difficult to account for in these conventional processes. This research aims to map and attempt to close the gaps in the areas of testing and approval of such automated and connected vehicles. During our research into the homologation process of traditional vehicles; functional safety issues, challenges of artificial intelligence in safety critical systems, along with questions of cyber security were investigated. Our process focuses on the integration of the already existing functions and prototypes into new products safely. As a key result, we managed to identify the main gaps between information and communication technology and automotive technology: the rigidity of the automotive homologation process, functional safety, artificial intelligence in safety critical areas and we propose a solution.

Intelligent Transportation System has been the driving forces to enable the paradigm of autonomous vehicles, smart roads and Internet of Things (IoT). For the safety and security of the traffic and transportation, stabilization of the technology and system is necessary. In addition to this, security of intelligent transportation system also influences the smart security and safety of vehicles, pedestrians and drivers. Thus, it is one of the most important application for the daily technology. There has been significant study related to security in vehicular network systems for intelligent transportation system usages. In this study, smart road and intelligent transportation system terms were explained. Attacks and threats of intelligent transportation system were evaluated with their security solutions while security objectives and architecture of intelligent transportation system and smart road were examined. During the evaluation, The European Telecommunications Standards Institute security standards were considered. It is possible to deduce that with developed technology, attack and threats level will be much bore pre-cariousness. New threats and attacks have to be investigate and simulate to find the solution for them.

As automated driver assistance functions are getting more and more popular, they will surly have a significant impact on our life especially considering security and the expected serious effects of malicious interventions. In light of the introduced aspects, the Test Field of Zalaegerszeg has started a research to evaluate the required professional and scientific framework to prevent transport systems from malicious external intervention. With regard to this aspect, the homologation system has been analyzed. In accordance with this the main objective of the article is to develop integrity level structure related to transport systems especially focusing on security issues. In this context the paper reconsiders the structure of ASIL to provide a proper framework focusing on the outstandingly important issues of cybersecurity. To develop a novel SIL framework a tailormade method is identified to define risk parameter values related to certain SIL categories. At the end of the research, the investigation has determined the acceptable hazard rates related to the S&SIL architecture.

Highly automated and autonomous vehicles become more and more widespread changing the classical way of testing and validation. Traditionally, the automotive industry has pursued testing rather in real-world or in pure virtual simulation environments. As a new possibility, mixed-reality testing has also appeared enabling an efficient combination of real and simulated elements of testing. Furthermore, vehicles from different OEMs will have a common interface to communicate with a test system. The paper presents a mixed-reality test framework for visualizing perception sensor feeds real-time in the Unity 3D game engine. Thereby, the digital twin of the tested vehicle and its environment are realized in the simulation. The communication between the sensors of the tested vehicle and the central computer running the test is realized via the standard SENSORIS interface. The paper outlines the hardware and software requirements towards such a system in detail. To show the viability of the system a vehicle in the loop test has been carried out.

The paper proposes an eco-cruise control strategy for urban public transport buses. The aim of the velocity control is ensuring timetable adherence, while considering upstream queue lengths at traffic lights in a probabilistic way. The contribution of the paper is twofold. First, the shockwave profile model (SPM) is extended to capture the stochastic nature of traffic queue lengths. The model is adequate to describe frequent traffic state interruptions at signalized intersections. Based on the distribution function of stochastic traffic volume demand, the randomness in queue length, wave fronts, and vehicle numbers are derived. Then, an outlook is provided on its applicability as a full-scale urban traffic network model. Second, a shrinking horizon model predictive controller (MPC) is proposed for ensuring timetable reliability. The intention is to calculate optimal velocity commands based on the current position and desired arrival time of the bus while considering upcoming delays due to red signals and eventual queues. The above proposed stochastic traffic model is incorporated in a rolling horizon optimization via chance-constraining. In the optimization, probabilistic guarantees are formulated to minimize delay due to standstill in queues at signalized intersections. Optimization results are analyzed from two particular aspects, (i) feasibility and (ii) closed-loop performance point of views. The novel stochastic profile model is tested in a high fidelity traffic simulator context. Comparative simulation results show the viability and importance of stochastic bounds in urban trajectory design. The proposed algorithm yields smoother bus trajectories at an urban corridor, suggesting energy savings compared to benchmark control strategies.

Vissim is a microscopic road traffic simulator based on the individual behavior of vehicles. The goal of the microscopic modeling approach is the accurate description of the traffic dynamics. Thus, the simulated traffic network may be analyzed in detail. The simulator uses the so-called psycho-physical driver behavior model developed originally by Wiedemann (1974). Vissim is widely used for diverse problems by traffic engineers in practice as well as by researchers for developments related to road traffic. Vissim offers a user friendly graphical interface (GUI) through of which one can design the geometry of any type of road networks and set up simulations in a simple way. However, for several problems, the GUI is not satisfying. This is the case, for example, when the user aims to access and manipulate Vissim objects during the simulation dynamically. For this end, an additional interface is offered based on the COM which is a technology to enable interprocess communication between software. The Vissim-COM interface defines a hierarchical model in which the functions and parameters of the simulator originally provided by the GUI can be manipulated by programming. It can be programmed in any type of languages which is able to handle COM objects (e.g. C++, Visual Basic, Java, etc.). Through Vissim-COM the user is able to manipulate the attributes of most of the internal objects dynamically.

In the future, the presence of highly automated vehicles is expected to become more and more wide spread. In such systems, the whole driving task will be performed by the vehicle autonomously, thus, vehicles must be able to control their motion in various circumstances, even at stability limits. In this paper, the authors consider the control of a steady-state drifting maneuver, which means saturated rear tire forces. In a previous article, a MIMO linear quadratic regulator (LQR) controller was designed, and it showed good performance in simulation environment. The test results of a real vehicle implementation are presented here, which was the logical next step of the work. For the vehicle platform, a series production sports car was chosen. Modifications were made in order to enable by-wire control. After identifying the vehicle model parameters through measurements, the control algorithm was implemented on a rapid prototyping unit. Vehicle states were measured with a high precision dual antenna GNSS module with RTK correction. Additionally, other dynamic parameters from the vehicle CAN bus signals were also used. The main goal was to stabilize different drifting equilibria, which showed satisfying performance of the proposed controller in a real vehicle setup as well.

Autonomous vehicles are at the forefront of interest due to the expectations of changing transportation for the better. In order to make better decisions on the road, vehicles use information from various sources: their own sensors, messages arriving from surrounding vehicles and objects, as well as from centralized entities-including their own Digital Twin. Certain decisions require the information to arrive with low latency and some of this information (such as video) requires broadband communication. Furthermore, the vehicles can populate an area, so they can represent mass communication endpoints that still need low latency and massive broadband. The mobility of the vehicles obviously requires the complete coverage of the roads with reliable wireless communication technologies fulfilling the previously mentioned needs. The fifth generation of cellular mobile technologies, 5G, addresses these requirements. The current paper presents real-life scenarios-on the M86 highway and the ZalaZONE proving ground in Hungary-for the demonstration of vehicular communication with 5G support, where the cars exchange sensor and control information with each other, their environment, and their Digital Twins. The demonstrations were carried out through the Scenario-in-the-Loop (SciL) methodology, where some of the actionable triggers were not physically present around the vehicles, but sensed or simulated around their Digital Twin. The measurements around the demonstrations aim to reveal the feasibility of the 5G Non-Standalone Architecture for certain communication scenarios, and they mainly aim to reveal the current latency and throughput limitations under real-life conditions.

In the first half of the 21st century, transportation that has been slowly changing over many decades has been transforming at an accelerated rate. Over the course of these few years, there will be more changes and more challenges to overcome. For a century it was unquestionable that a vehicle is driven by a driver and its energy comes via diesel or petrol from crude oil. Today vehicles’ autonomy in driving is increasing, and instead of crude oil-based fuels first, bio components and gaseous fuels appeared, and now electricity knocks at the door. The proliferation of electric driving, the new modes of vehicle use, and the autonomy and connectivity of vehicles represent new directions that challenge not only the automotive industry, but also vehicle users and regulators, and the states. New technologies bring about new security and safety challenges as well. Most of the challenges pop up in the cyber security domain. And its result is that a closer cooperation is necessary between the automotive industry and informatics. As these two leading industrial fields have a different setup, the cooperation is energy demanding task for all participants. Modification and upgrade of the homologation process seems to be one of the potential gateways that could merge the safety requests. Improving traditionally rigid automotive homologation processes needs a lot of extended test opportunities. In our research, eighlight the challenges posed by self-driving cars and show some trends briefly, and then examine the challenges posed by vehicle technology moving towards self-driving, and towards digitizing. The certification process of the automotive industry is highlighted and modifications are proposed. We propose to extend the traditional proving ground based certification processes with special, autonomous vehicles designed processes that are partially made within the virtual reality-proving ground mixtures. A newly designed proving ground not only offers a wide range of vehicle and traffic tests for conventional, connected and automated vehicles, but can also be used to test possible prototype solutions, as well as helps to develop the type-approval process, and useful for educational purposes. Cybersecurity has special dimensions, newly developed test environment is necessary to validate the vehicles and their elements. A complete vehicle testing and validation center is proposed to establish for automotive cyber security features, focusing not only on known, but also on unknown vulnerabilities. It will help to develop dedicated tests to eliminate unknown vulnerabilities and potential new vulnerabilities.

Autonomous Vehicles (AVs) are vehicles able to imitate human driving, control skills, and the ability to perceive the environment around them while driving on the road. Therefore, one of the techniques that should be studied in detail is the overtaking maneuver, which is one of the most dangerous scenarios for AVs on the road, since overtaking generates variations in the acceleration and deceleration of vehicles and changes in the normal circulation of vehicles when they change over to another lane. The overtaking maneuver consists of overtaking another vehicle which is traveling in the same direction at a slower speed. The article proposes a simulation of an overtaking maneuver scenario with AVs through the use of simulation software denominated PreScan, which incorporates an Adaptive Cruise Control (ACC) and Technology Independent Sensors (TIS) in order to carry out the overtaking maneuver. The results showed that AVs are capable of performing overtaking maneuvers in an urban environment that involves conventional vehicles. Therefore, using software that simulates AVs overtaking maneuvers becomes a useful tool to interpret AVs performance with the obstacles that exist on the road.

The paper presents a novel simulation concept for autonomous and highly automated road vehicles, called Scenario-in-the-Loop (SciL) testing. SciL can contribute to a more efficient development, testing and validation of driverless cars, which is a pressing question of our days. SciL based testing introduces a new approach capable to simulate and control realistic traffic scenarios around the autonomous vehicle under test realizing a Digital Twin technology for testing. For realistic traffic generation a high fidelity microscopic traffic simulator (SUMO) and for visualization the Unity 3D game engine are involved. The proposed testing methodology was proved with a real world autonomous car. As a test environment for SciL demonstration ZalaZONE Smart City Zone was used. Two different traffic scenarios (platooning and valet parking with pedestrian dummy) have been successfully tested and demonstrated.

The development of autonomous vehicles nowadays is attractive, but a resource-intensive procedure. It requires huge time and money efforts. The different carmakers have therefore common struggles of involving cheaper, faster and accurate computer-based tools, among them the simulators. Automotive simulations expect reality information, where the recent data collection techniques have excellent contribution possibilities. Accordingly, the paper has a focus on the use of mobile laser scanning data in supporting automotive simulators. There was created a pilot site around the university campus, which is a road network with very diverse neighborhood. The data acquisition was conducted by a Leica Pegasus Two mobile mapping system. The achieved point clouds and imagery were submitted to extract road axes, road borders, but also lane borders and lane markings. By this evaluation, the OpenDRIVE representation was built, which is directly transferable into various simulators. Based on the roads’ geometric description, a standardized pavement surface model was created in OpenCRG format. CRG is a Curved Regular Grid, containing all surface height information and objects, but also anomalies. The 3D laser point clouds could easily be transformed into voxel models, then these models can be projected onto two vertical roadside grids (ribbons), which are practically an extension to the OpenCRG model. Adequate visualizations demonstrate the obtained results.

The driver assistance systems becomes more common in today’s vehicles, and in the future highly automated vehicles and large number of autonomous vehicles will appear in roads. In the beginning these systems only support the human driver, but later they will able to control the vehicle driving. Hence vehicles have to plan the path, and follow it accurately and safely. Furthermore it has to provide convenience for the passengers. To find a better trajectory following controller first it is necessary to test it in simulation environment, then to test it in real environment in a vehicle. The subject of this article is an implementation of a lateral dynamic bicycle model, and an implementation of different controllers.Besides it will contain the testing of the controllers. If the controllers work with sufficient precision in the simulation, it will be tested in a real vehicle. The essence of the test is the speed limit, where the simulation and the real test starting to deviate. In the end of the article we compare the performance of the different controllers and evaluate which is the most appropriate. Besides the difference will also be evaluated between the simulated results and the results on the real vehicle. This comparison reveal which is the speed limit, as long as the values calculated on the lateral dynamic bicycle model approximate the actual results in a simulation environment with sufficient precision.

Autonomous driving is an old desire of mankind. The main purpose of this article is to present an autonomous lane changing method, that enables fully automated lane changing if given safety criteria are met. Environment detection is executed by multiple sensors such as radar, LIDAR and a smart-camera. Intrinsic sensors of the vehicle are utilized as well to ensure a smooth transition between lanes. The test results obtained on a closed test track prove the usefulness of the presented methodology. The resulting algorithm and experiences collected during implementation could be useful for integration into a larger system.

In the future, the appearance of highly automated vehicles is expected, the driver assistance systems will become more common in the vehicles. Initially, these systems are only supported the driver, but increasingly take over the control of the entire vehicle. In this practice, the vehicle must plan the route and follow it safely while providing a comfortable journey for passengers. In order to develop the path following controller, it is first tested in a simulation environment and then on a real vehicle. In a previous paper, authors have implemented different path following controllers that were tested on a demonstration vehicle after testing in the simulation environment. In the simulation environment, the controllers were tested on a dynamic bicycle model, which model describes the lateral dynamics of the vehicle. The simulation and measurement results showed a difference. The subject of this paper is to find out the reason of the difference that was identified in the dynamic behavior of the steering system, including the effect of the tires. Based on these, the vehicle model was adjusted. Measurements were made to identify the dynamics of the steering system, and the tires. Analyzing the results of the measurements, the dynamic model of the steering system was described, the vehicle model has been expanded on this basis. Finally, the path following simulation with the improved model was calculated, which was compared to the real vehicle measurement and the results showed good correlation.

In the future, vehicles with a high level of automation are expected to become more popular. These systems are increasingly taking control of the vehicle, and supporting the driving task. Vehicles with a high level of automation have the task of planning the path and following it safely. In this paper, the authors want to improve the quality of path following by further developing an already existing combined controller, by optimizing controller parameters and taking the two controllers into account with different weights. A vehicle model is implemented for this purpose, which also includes the dynamics of steering. The combined controller includes both pure pursuit and Stanley controllers, the accuracy of operation was tested by simulation.

In response to the changing social demand for safer and more efficient transport in the last few years the development of autonomous driving functions has increased dramatically and in so created many challenges for the automotive industry. Since autonomous driving functions must handle nearly countless traffic participants and various situations, most companies developing such technologies began testing on public roads which became possible due to the amendment of laws in several progressive countries or states worldwide leading the way, for example leading states such as California, The Netherlands and from 2017 Hungary [1]. However, recent regrettable incidences highlight the risks of public road testing and strengthen the role of closed proving grounds and specially designed and constructed controlled urban-like test areas which can represent the real-world environment.

As highly automated and autonomous vehicles (AVs) become more and more widespread, inducing the change of traffic dynamics, significant changes occur in traditional traffic control. So far, automotive testing has been done mostly in real-world or pure virtual simulation environment. However, this practice is quite obsolete as testing in real traffic conditions can be quite costly, moreover purely simulation based testing might be inadequate for specific goals. Accordingly, a hybrid concept of the Vehicle-inthe-Loop (ViL) was born recently, in accordance with the Hardware-in-the-Loop concept, i.e. in the ViL concept the vehicle is the 'hardware' within the simulation loop. Furthermore, due to the development of software capabilities, a novel approach, the Scenarioin-the-Loop (SciL) concept evolves based on the ViL approach. The paper defines the main purposes and conditions related to implementing ViL and SciL concepts from the perspective of traffic simulation and traffic control.

Based on the reviewed literature, the objective of the article is to investigate the most important cyberattack factors affecting the effectiveness of the connected and autonomous transport system. Accordingly, remotely implemented malicious interventions are in the focus of our study, especially considering cyberattacks on connected and autonomous vehicles. To introduce the expected impact of the analysed cyberattacks, certain cyberattack types are demonstrated in the paper. In the paper, malicious interventions are assumed to attack the transport network contrary to the individual road users, which assumption can strongly increase the risk of a given cyber-incident. To illustrate the effect of the evaluated cyberattacks, the transport system of Budapest is used. According to the results, some of the interventions might not be effective, while some of them can expectedly significantly increase travel time values of the network.

The assurance of process safety plays an important role in the field of information technology. Securing the information has become one of the biggest challenges in the present day. Whenever we think about the protected systems the first thing that comes to our mind can be malicious interventions which are increasing immensely day by day. Nowadays we live the world of huge automotive developments with the appearance of the demand for autonomous vehicles. On the other hand, technological developments also provide a lot of advantages for the society. The benefits of autonomous cars include reduced mobility costs, increased safety, increased mobility, significant reduction of traffic collisions. However, it cannot be forgotten that the extension of cyberspace affects the transportation increasingly. Accordingly, cars are produced with high level of connectivity and automation. Therefore, the risks arriving from the cyberspace can now endanger the safe and secured transportation. These tendencies shall motivate manufacturers and developers to permanently improve the ability of vehicles to protect themselves and their passengers.

The presence of highly automated vehicles and driver assistance systems is expected to grow in the future. In recent years, these systems offer assistance for the driver but, in the future, the human driver will be increasingly replaced by automated vehicle functions. In order to achieve this, the vehicle must be able to plan and safely follow a trajectory. In this paper, a MIMO linear quadratic regulator was designed for stabilizing the vehicle during steady-state drifting. The research can be utilized in different fields of applications. Namely, the driving capabilities of an automated vehicle must be at least as good as a human driver. Hence, these systems must be able to drive the car at friction limits. Furthermore, a better understanding of the dynamics of drifting and the equilibrium conditions can be used in motorsports.

Nowadays, besides the conventional systems, the automated functions of the automotive industry are becoming more and more important. With the emergence of autonomous vehicles, more emphasis should be placed on safe testing. The complexity of the systems makes the tests more difficult and complex. This is made possible by the creation of tests in the simulation environment. There are a lot of advantages to the simulation environment, because it is cheaper and the test scenario is much faster and more adaptable before the real environmental tests take place. The article deals with the advantages and disadvantages of test cases in the simulation environment (PreScan), with particular emphasis on recognizing traffic signals.

Road networks are vulnerable to disasters both natural and manmade caused disasters. Damage caused by disasters on a given set of network element can affect the operability of the whole transportation system. However, not all network elements equally affect system; typically some elements are more critical to the network functioning than the others. The paper aims to study models that can evaluate the effect of a given network element on the vulnerability of a road infrastructure system. Three models will be chosen for the study; shortest paths, maximum flow and a Hydrodynamics model.

Thanks to the recent evolution in science and technology, road transport is going to change radically. Today vehicle assistant systems help the human driver to manoeuvre the vehicle safely. In the near future connected and highly automated vehicles will also appear on the road in multiple transport modes. Higher vehicle automation levels rely on disruptive technologies that simply cannot be tested and approved in the currently used sustaining technologies format. As validation processes become more complicated they require a more specific and ever-changing test environment. While current autonomous vehicle test environments are developed around activities that mostly concentrate on an urban test area, every single element of the new Hungarian automotive proving ground is dedicated to the testing and validation of connected and automated vehicles. This paper will discuss the design concept, illustrating the unique points that differentiate the new Hungarian test facility from that of a conventional vehicle test track as well as from other current autonomous vehicle testing areas.

With the recent emergence of new and statistically safety-enhancing ADAS (Advanced Driver Assistance) systems and autonomous driving technologies-in particular machine learning based models-the risks we face are also changing, and their properties are often still poorly understood. This gives rise to new kinds of mistakes and more worryingly, new vectors of attack exploiting these new vulnerabilities. In this work, we present a serious, but still unexplored security threat in the form of adversarial stickers that can be distributed over the Internet, then printed and easily deployed on traffic signs by malicious layman attackers. Our road sign stickers differ from other similar approaches in that they require no expertise in their fabrication or application, and thus present a much more severe threat to traffic safety.

Fuel consumption prediction and recharging strategies are crucial fields of autonomous hybrid vehicles. In our paper based on literature review the currents trends of hybrid vehicles are compared and working principals of hybrid strategies created for different types of hybrid drivetrains were described. The fuel management, basics of the range estimation in hybrid vehicles are displayed. Analyzations of the factors affecting the fuel consumption and energy management were made, along with an estimation of the order of magnitude of these factors, based on experiments done by EPA. Main fuel consumption influencers are listed and a real word test is carried out in a highway measurement. It was focusing on the affection on the fuel consumption, caused by the additional consumers of the vehicle has been carried out. Evaluation of this measurement was made and conclusions were drawn about the efficiency changes of the vehicle.

The accurate identification and recognition of horizontal and vertical traffic signs can cause problems for autonomous vehicles. That is why have checked anomalies of traffic signs in the topic. Because for the safely navigate an autonomous vehicle, it is necessary to have a very accurate environment recognition. However, there are many problems with the sensors recognitions ability. The current article presents a test using a previously created anomaly classification table. The table divides the signals into five anomalies groups that are real and can cause problems with the recognition systems. This article with the tests highlights the limit values and circumstances that make it challenging to identify the traffic sign. The environment, weather, and visibility conditions, as well as different traffic situations, are typical problems. The test was done on the ZalaZONE, SMART city track in Hungary. The study can further develop a critical test environment for traffic signs to help develop future autonomous vehicle systems. It can use for environment recognition systems tests and validation procedures.

As autonomous and driver assistance functions become more and more complex, novel testing methodologies are required. Introduction of highly automated and autonomous vehicles on public roads alters traffic flow dynamics. Therefore, it is not enough to test only the single vehicle itself, but it will also be required to test its interaction with the environment and other vehicles of traffic. In this context, a practical goal is to test independently of real traffic as much as possible. The Vehicle-in-the-Loop method (i.e. where the vehicle is the ’hardware’) is already used for testing vehicles. In this configuration the vehicle is directly forced to do specific manoeuvres, but the sensors of the vehicle are not used and the vehicle does not have to make decisions. The concept of the Scenario-in-the-Loop introduces a testing method where the physical attributes as well as the sensors of vehicles are tested via a virtual twin. In the paper we present the main principles of the Scenario-in-the-Loop concept and outline the requirements of such a test environment from the perspective of traffic control.

With the emergence of autonomous vehicles, safety and environmental sensing systems are becoming increasingly important that support drivers. For the autonomous functions and the complexity of the systems, more emphasis should be placed on testing and safe implementation on advanced driver-assistance systems (ADAS) systems. This is made possible by the preparation of tests in the simulation environment. There are some advantages to the simulation environment because it is cheaper and the test scenario is faster, safer and adaptable before the real environmental tests. This article describes how to test, environment sensors, including traffic sign recognition systems, using our already designed scenario. All this with a PreScan camera and a Neural Network (NN) for the classification. It is also possible to place the damaged traffic signs, sensors also sensors can be tested in critical situations, under extreme conditions. This article also points out when it is worthwhile and when it is not advisable to rely on simulation tests.

Cybersecurity is becoming more and more relevant. Autonomous vehicles handle large amounts of data and can connect to more and more existing devices, smartphones, tablets, or even other cars and systems. This poses the risk of unauthorized access to data. Theoretically cars have separate computer units, operate in isolation, and are not connected, so there is less possibility to be attacked. However if the vehicles are interconnected, hackers can have easier access to personal data. They can get information about the location of the car owner, their typical trips, and, for example, allow an intruder to know when the tracked person is not at home. Furthermore it can also be happened that the vehicle operation is maliciously disturbed, which can result in a security risk for the passengers. In extreme cases, computer terrorist attacks can also be prepared - large-scale interventions on roads can lead to chaos across a region or country. In accordance with the introduced threats, it is a crucial objective of this research to indicate specific methods, which can help the industry to evaluate and prepare for these kinds of attacks in a proper way.

With the advent of cypher-physical (systems of) systems, new challenges for safety and security arise. Especially in the context of autonomous driving we are currently facing a complex environment, where security problems can easily result in safety-relevant issues, and vice versa. There have been multiple approaches in the past to combine the approaches from safety and security best practices into a combined view, all with their individual challenges. We propose a fully integrated approach, combining safety with security and modelling their complex interactions. In this work we start by giving a thorough definition of the basic terms and concepts used in safety and security, in order to identify similarities and differences. We then propose and outline a combined view on the safety and security causal chains and define their interdependencies.

Data is the new oil for the car industry. Cars generate data about how they are used and who is behind the wheel which gives rise to a novel way of profiling individuals. Several prior works have successfully demonstrated the feasibility of driver re-identification using the in-vehicle network data captured on the vehicles CAN (Controller Area Network) bus. However, all of them used signals (e.g., velocity, brake pedal or accelerator position) that have already been extracted from the CAN log which is itself not a straightforward process. Indeed, car manufacturers intentionally do not reveal the exact signal location within CAN logs. Nevertheless, we show that signals can be efficiently extracted from CAN logs using machine learning techniques. We exploit that signals have several distinguishing statistical features which can be learnt and effectively used to identify them across different vehicles, that is, to quasi reverse-engineer the CAN protocol. We also demonstrate that the extracted signals can be successfully used to re-identify individuals in a dataset of 33 drivers. Therefore, not revealing signal locations in CAN logs per se does not prevent them to be regarded as personal data of drivers.

Autonomous driving vehicles should be able to control themselves when handling limits have been reached in order to increase safety and robustness. The aim of the present work is to build and validate a mathematical model for car drifting that can be used as a base for further development of hardware and software. This paper describes the mathematical description of the vehicle and tires assuming certain constrains, the setup for testing on a real car performing a defined complex trajectory at the limits of handling. Results prove accurate enough tracking of the measured parameters while still maintaining simplicity.

The main goal of this article is to determine a comprehensive and well applicable model architecture, which is adequate to estimate the system level advantages with regard to automated transportation and which is appropriate to determine possible costs and losses with regard to the approach of such transport modes. In the study the Budapest Transportation Model is applied. Taking autonomous vehicle penetration into account as an external variable, in the analysis a constant growth is assumed in the penetration of automated vehicles. This article has taken the most relevant factors of transportation network into account with regard to automated cars. It is also important to mention that the paper presents the most important modelling phases, where automated cars can be taken into account during the macroscopic modelling process. In the first step of the process during the network definition phase it is possible to consider the effect of automated vehicles on the transport system (e.g. separated routes). The next phase where the effect of automated vehicles should be taken into consideration is the mode choice step (e.g. different demand segments). And finally traffic assignment step, where the effect of automated vehicles can be represented. The easiest way for this is the modification of passenger car units through the parameter of assigned traffic per capacity ratio.

The objective of this study is to provide an efficient methodology for identifying the major factors contributing to road accidents considering both their spatial and environmental attributes. In the first step, K-mean clustering has been used to define homogeneous accident clusters based on their spatial distribution. Since accidents occurring at close distances are likely to share the same environmental and causal attributes. Secondly, the Empirical Bayes (EB) method has been applied to define and rank the high-risk accident segments based on the determined clusters. The impact of various parameters involved in accidents occurrence has been examined using the decision tree method. The spatial identification of high-risk road sites and their contributing factors can highly help road safety experts in identifying accident's patterns on road and so proper measures can be taken to improve road safety.

The development of autonomous vehicles requires different sensor types for constructing an accurate local environment model. The evolution of sensor technology is challenging the development of sensor data fusion. Although, the sensors are getting more robust and reliable. However, because of the development of sensors the data fusion should be able to handle diversified sensor clusters. Thus, the high-level sensor fusion algorithms, like track to track method, are getting more important. This paper introduces different tracking approaches, which are intended to support a track-to-track fusion method of an autonomous vehicle in highway situations.

In our days, vehicle automation is in a continuous evolutionary phase consisting of experiments, testing and validation. Accelerating the development and deployment of autonomous vehicles and infrastructure is a real demand as these technologies have a great potential to improve traffic safety and resolve road transport problems. The Vehicle-In-the-Loop testing is therefore indispensable throughout the development process. As a potential solution to this need, this paper introduces a new approach for test environment capable to simulate realistic traffic around the autonomous test vehicle. The test car therefore can be put into virtual transportation network by applying real-time microscopic traffic simulation, i.e., the method enables safe vehicle testing. The proposed test environment was proved with real world autonomous car.

The development of autonomous vehicles requires algorithms responsible for constructing the local surroundings of the vehicles. The algorithm responsible for creating a common model from the sensors' detection is the so called sensor fusion method. The performance of these methods should be compared to each other, using a common metric, in order to select the proper algorithmic approach, sensor set or sensor placement. This paper describes the evaluation of a track to track sensor fusion algorithm on a dataset recorded on highways.

The OpenCRG road surface model as part of an open available data format is originally designed for simulation purposes, where the micro level information about the road surface is stored in this format. The paper introduces the whole open data format family (OpenDRIVE, OpenCRG and OpenSCENARIO) and then demonstrates that an efficient road surveying technology, namely the terrestrial laser scanning (TLS) is excellent data source to create realistic description about the road surface. The TLS captured point cloud stores very fine details about the surface, including height irregularities, potholes, cracks, manholes. Because the consortium being behind the developers contain car manufacturers (e.g. Daimler, BMW), mapping agencies (e.g. Here) and technology companies (e.g. Fraunhofer), the usage of the OpenCRG format is expected not only in laboratory circumstances, but later in real life applications. Autonomous vehicles have extreme requests for detailed information about the road and its neighborhood, and the developed open format seems to have excellent support to self-driving vehicle control.

In the future there will be a lot of changes and development concerning autonomous transport that will affect all participants of transport. There are still difficulties in organizing transport, but with the introduction of autonomous vehicles more challenges can be expected. Recognizing and tracking horizontal and vertical signs can cause a difficulties for drivers and, later, for autonomous systems. Environmental conditions, deformity and quality affect the perception of signals. The correct recognition results in safe travelling for everyone on the roads. Traffic signs are designed for people that is why the recognition process is harder for the machines. However, nowadays some developers try to create a traffic sign that autonomous vehicles can use. Computer identification needs further development, as it is necessary to consider cases where traffic signs are deformed or not properly placed. In the following investigation, the advantages and disadvantages of the different perception methods and their possibilities were gathered. A methodology for the classification of horizontal and vertical traffic signs anomalies that may help in designing better testing and validation environments for traffic sign recognition systems in the future was also proposed.

Developing autonomous driving technologies is complex and needs to be approached from multiple directions. However, these vectors of development need to intersect at one point and our answer is ZalaZONE. This proving ground design is unique when it comes to the development of automated driving technologies and has grass-root origins in higher education and industry. However, the proving ground is not the total solution, but acts as a catalyst and synergistic hub for the different directional vectors addressing autonomous vehicle development. ZalaZONE in its role as an education and research centre from the secondary to the highest trinary educations levels supports this basic progressive vector and creates the first layer. A second layer concerns the proving ground’s core function, being used not only for classical dynamic testing, but focusing on the evaluation of the most troublesome “use cases” that simulation indicates. The evaluation of these indicated “use cases” will required autonomous vehicles to be tested at their dynamic limits in a 100% controlled and safe environment. Similarly, the proving ground is designed to calibrate and validate current and future simulation systems, also a significant technological metamorphosis. Public road testing is a third layer or point of axis supported by the PG. Legislative changes in Hungary offer a flexible regulatory environment within Europe for autonomous vehicle testing and this allows the PG to be a one stop hub for launching test in various environments, from live urban to the usage of a tri-national testbed road network in the Hungarian, Slovenian, Austrian enclave. Fourth and fifth layers nurtured by ZalaZONE are “sample vehicles fleet services” and “crowd-sourced traffic cloud” data collection and analysis. This paper will discuss all these 5 complex and integrated axis points and how ZalaZONE provides this synergistic role in this new Multi-layer autonomous vehicle and simulation validation Ecosystem.

Accident analysis and reconstruction classically based on tracks recorded during the scene investigations of an accident. In a case ideal for the post reconstruction of the accident, these traces can greatly help reconstruction of the accident process. However, in most cases, it is not possible to record all necessary traces, so it is not possible to use these data later. There are tools from the 1950s - with continuously evolving features, - that could partially respond this problem, for example by continuous monitoring certain vehicle dynamics parameters, and by recording these parameters at a time of an accident occurring, useful information can be gained about the vehicle’s state of motion. The data provided by accident data recorders can thus provide useful information on accidents involving traditional vehicles, but their significance is growing especially with the spread of highly automated and autonomous vehicles, where the control of the vehicle can, in certain circumstances, be transferred to the vehicle. In this article we will present the data that can be used for modern accident analysis, then we will investigate accident data recorded during crash tests, which were collected from the vehicles own control units with EDR functions, and by the post-fitted event data recorders during collision attempts. On the basis of our experiences and conclusions, we will make suggestions on the range of optimal data from the point of view of accident analysis, on the recording of these data, paying particular attention to autonomous vehicle specific solutions.

Accident reconstruction is the process of reliably discovering what has happened before a serious event. We show how the most widely used intra vehicular network (namely the Controller Area Network, CAN) can be used in this process. We show how the actual velocity and steering wheel position transmitted on the CAN network can be used to reconstruct the trajectory of a vehicle. This trajectory is an essential input in the reconstruction process. In this paper, we show how the CAN traffic of an actual vehicle can be used to reconstruct the trajectory of the vehicle, and we evaluate our approach in several real life experiments including normal and pre-accident situations.

Technologies are already available using which road vehicles are able to operate in a completely autonomous way under certain circumstances. These technologies and current developments make it likely that within a few decades fully autonomous vehicles will spread in road traffic. This is expected to greatly increase traffic safety, provide mobility to groups currently excluded from transport, make transport more economical and reduce pollution. In parallel these, however, it can be stated that - despite the fact that in the case of conventional vehicles the driver is the most safety-critical element of the system, - autonomous vehicles will also be involved in traffic accidents, which will require the legal assessment of liability and the determination of the level of compensation. In this paper, we present how current regulations and practices can be applied to accidents affecting autonomous vehicles and we propose a methodolody for the liability distribution that was formerly developed for a mobile robots.

The Research Center for Autonomous Road Vehicles (RECAR) was founded in 2015 upon the initiative of the Faculty of Transportation Engineering and Vehicle Engineering of Budapest University of Technology and Economics. The research center is supported by industrial partners and other academic partners targeting research and educational purposes. In complement to this project, the construction of a new automotive test track is also under development especially for autonomous road vehicle testing serving as automotive proving ground in Zalaegerszeg, Hungary. Accordingly, an intensive research has been started in RECAR center in the field of autonomous vehicle technology. The paper’s goal is to share the main practical and methodological experiences with the scientific audience as well as the industrial sector. Based on the initial research actions we intend to enlighten the upcoming research challenges of driverless vehicles and automated intelligent transport system. Basically, three main topics are concerned. Firstly, the main issues concerning autonomous vehicle research are summarized. Secondly, the requirements for autonomous test track design are concluded. Thirdly, the legal questions that emerge with the appearance of driverless vehicles are investigated, especially concerning liability.

The objective of this paper is to define a generally usable methodological framework, which is appropriate to predict the network level benefits related to autonomous systems and which is adequate to identify possible expenses related to the introduction of such a system. During the research the Unified Traffic Model of Budapest has been used. Considering autonomous vehicle penetration as an external parameter, during the investigation a permanent increase has been considered in the number of autonomous cars. This paper has considered the most important parameters of transport systems with connected and autonomous vehicles but there are many other macroscopic modelling aspects of emerging mobility which can be taken into account in the future phases of this research. According to the result of the investigation, it can be concluded that in case of expecting 100% penetration 1-2 billion saved hours can be achieved in a 15-year-long assumed operation time.

The development of automotive technologies and transport systems brings not only new features such as the emergence of autonomous vehicles and an advanced urban environment, but is a major challenge for the transport sector professionals. Recognizing traffic signs in a real world can cause a lot of difficulties for autonomous systems. Illumination, environmental conditions, deformity and quality are affecting for the perception of signals. The accuracy of recognizing signal systems is essential for safe travelling. Traffic signs originally have been designed for human interpretation rather than to machine recognition. Computer identification needs further development, as it is necessary to consider cases where the traffic signs are distorted or not properly placed. In our investigation we collected the advantages and disadvantages of the different perception methods. We also proposed a methodology for the classification of different traffic sign anomalies, that may help in designing better testing and validation environments for traffic sign recognition systems in the future.

The development of automotive technologies brings new features such as the emergence of autonomous vehicles and an advanced infrastructure, but is a big challenge for the professionals. In this paper we present the currently used signs on the different road sections. We describe the significance of signaling systems for the present and future traffic, especially from the point of view road safety and environment. We analyzed the used traffic signs recognition systems how them can identify the signals in the traffic. Particular emphasis is placed on the benefits, the importance and the problems that arise in recognizing systems. We will propose their development opportunities, and these will affect the pre-emergence period of fully autonomous vehicles when the vehicles are on lower autonomy level but at the same time the higher autonomy level vehicles are appear in the traffic. It has difficulties with the recognition systems and the emergence of autonomous vehicles. There may be problems with systems in different signaling systems. There are some other problems what need to find solution for example is the legal rules to the autonomous vehicles and different country has different looking traffic signs what is hard to recognize to the systems.

High Definition (HD) maps are newly defined maps with remarkably extended content. These maps are based on 3-dimensional surveys of the environment and are stored in a structured way to fully support today's vehicles. The basic map content focuses not only on the lanes, but also on the road neighborhood. The primary information is therefore the connections where the vehicles can travel, while the secondary content efficiently supports the exact positioning of the vehicles and describes the relation of the roads to their environment. The current paper demonstrates a pilot study of creating HD map in urban and rural environment. The surveying technology is based on terrestrial laser scanning, where the obtained point clouds are evaluated and transformed into map content. The newly developed information storage model is able to support the vehicle control.

A vehicle was developed to be capable of demonstrating autonomous vehicle functionalities at Budapest University of Technology and Economics. The purpose of the development was to build up a proper platform for the demonstration of results in autonomous vehicle related researches. In this paper the vehicle preparation will be presented. In the first phase of the development the total electronic controllability was targeted as well as some environment sensing capabilities to ensure basic functionalities like lane keeping, adaptive cruise control, collision avoidance and higher level functions like traffic jam assistant, traffic jam pilot and home zone assist. The project is the first research activity of the RECAR research program. By having this autonomous vehicle demonstration platform ready the research center will have several opportunities to prove, test and evaluate further results in a real vehicle.

A world of autonomous cars is at our doorstep, arising new challenges for engineers. The sentence: "Safety first!" is more than valid in this field. One of the main safety features of a car is the braking system. However, the brake by wire concept is rather new and there are several issues with it. Our article deals with the controller design phase for a smart braking system, where the braking actuator is modeled as a system having multiple uncertainty sources. The design is verified using simulations and the final verification phase includes testing on a board model of the actuator system.

A vehicle was developed to be capable of demonstrating autonomous vehicle functionalities at Budapest University of Technology and Economics. The purpose of the development was to build up a proper platform for the demonstration of results in autonomous vehicle related researches. In this paper the vehicle preparation will be presented. In the first phase of the development the total electronic controllability was targeted as well as some environment sensing capabilities to ensure basic functionalities like lane keeping, adaptive cruise control, collision avoidance and higher level functions like traffic jam assistant, traffic jam pilot and home zone assist. The project is the first research activity of the RECAR research program. By having this autonomous vehicle demonstration platform ready the research center will have several opportunities to prove, test and evaluate further results in a real vehicle.

https://ercim-news.ercim.eu/en109/special/recar-hungarian-research-centre-for-autonomous-road-vehicles-is-on-the-way

Today's trends in road transport require the improvement of safety. Road vehicles have already had several driver assistant systems that can control the movement and the behavior of a vehicle. The development of these systems leads to a high level of automation. In the early steps the control systems have just helped the human driver, today more and more driving tasks can be delegated to them. At the end of the day driverless, connected and automated vehicles will appear in road transport. This paper will discuss the requirements for the longitudinal and the lateral control systems of vehicles. An application will also be presented about automated features in a passenger car.

Today’s vehicles already have several driver assistant systems and in the near future highly automated vehicles will also appear in road transport. Higher automation levels rely on disruptive technologies that cannot be tested and approved in the former way. To be able to guarantee future road safety also disruptive testing and validation methods are required. The complexity of the systems and the stochasticity of the potential traffic situations demand new approaches with different testing levels and approval layers. Since there are no off-the-self solutions available beyond the research the authors also participate in international activities like the Gear 2030 EU level initiative. This paper will discuss the proposed new approach for connected and automated vehicle testing methodology concluding with the technical specification results for the new Hungarian automotive proving ground.

In this paper, we propose a compression method that allows for the efficient storage of large amounts of CAN traffic data, which is needed for the forensic investigations of accidents caused by cyber attacks on vehicles. Compression of recorded CAN traffic also reduces the time (or bandwidth) needed to off-load that data from the vehicle. In addition, our compression method allows analysts to perform log analysis on the compressed data, therefore, it contributes to reduced analysis time and effort. We achieve this by performing semantic compression on the CAN traffic logs, rather than simple syntactic compression. Our compression method is lossless, thus preserving all information for later analysis. Besides all the above advantages, the compression ratio that we achieve is better than the compression ratio of state-of-the-art syntactic compression methods, such as zip.

In this paper, we propose a compression method that allows for the efficient storage of large amounts of CAN traffic data, which is needed for the forensic investigations of accidents caused by the cyber-attacks on vehicles. Compression of recorded CAN traffic also reduces the time (or bandwidth) needed to off-load that data from the vehicle. In addition, our compression method allows analysts to perform log analysis on the compressed data. It is shown that the proposed compression format is a powerful tool to find traces of a cyber-attack. We achieve this by performing semantic compression on the CAN traffic logs, rather than the simple syntactic compression. Our compression method is lossless, thus preserving all information for later analysis. Besides all the above advantages, the compression ratio that we achieve is better than the compression ratio of the state-of-the-art syntactic compression methods, such as zip.

High Definition (HD) maps are newly defined maps with remarkably extended content. These maps are based on 3-dimensional surveys of the environment and are stored in a structured way to fully support today's vehicles. The basic map content focuses not only on the lanes, but also on the road neighborhood. The primary information is therefore the connections where the vehicles can travel, while the secondary content efficiently supports the exact positioning of the vehicles and describes the relation of the roads to their environment. The current paper demonstrates a pilot study of creating HD map in urban and rural environment. The surveying technology is based on terrestrial laser scanning, where the obtained point clouds are evaluated and transformed into map content. The newly developed information storage model is able to support the vehicle control.

The most important problem of electric vehicles is the energy storage. Currently the system components are ready for mass production, the cost of energy storage, and energy density is limited by the capacity of these types of vehicles. The present study aimed to implement a battery monitoring system in to an F2000 electric formula car. Based on the real-time monitoring capability, the control system can continuously check the voltage of the battery. Monitoring of battery systems is a critical importance to maintain reliable operation of electric vehicles. In addition of fault diagnosis the charge of the battery, the status of the battery cells, measure of the load (current consumption) and also the load variation in time (peaky, constant) can be monitored real-time.

Today's road vehicles already have several driver assistant systems and in the near future connected and highly automated vehicles will also appear in road transport. Higher automation levels rely on revolutionary technologies that cannot be tested and approved in the former way. To be able to achieve better efficiency, comfort and to guarantee future road safety new testing and validation methods are required. The complexity of the systems and the stochasticity of the potential traffic situations demand new approaches with different testing levels and approval layers. This paper will discuss the preliminary steps for a proposed new approach for connected and automated vehicle testing methodology.

The aim of the paper is to give an overview on the existing eCall solutions for autonomous car accident detection. The requirements and expectations for such systems, considering both technological possibilities, legal regulatory criteria and market demands are discussed. Sensors utilized in e-call systems (crash sensing, systems for positional and velocity data, and communication solutions) are overviewed in the paper. Furthermore, the existing solutions for eCall devices are compared based on their level of autonomy, technical implementation and provided services.

The paper presents a vehicle framework for the development, implementation and testing of autonomous vehicle functions. The base vehicle is a Smart Fortwo which was strongly modified in order to be suitable for autonomous driving demonstration. The Sensor framework was expanded with environment sensors, such as lidar and radar, while full vehicle control was achieved by installing drivetrain, brake and steering actuators. The control framework of the vehicle consists of multiple modularly replaceable units. The first test cases perform middle level autonomous functions, while at the end of the paper, an in-development phase presentation of a lidar and odometry based positioning architecture is given.

In this paper, we propose a compression method that allows for the efficient storage of large amounts of CAN traffic data, which is needed for the forensic investigations of accidents caused by cyber attacks on vehicles. Compression of recorded CAN traffic also reduces the time (or bandwidth) needed to off-load that data from the vehicle. In addition, our compression method allows analysts to perform log analysis on the compressed data, therefore, it contributes to reduced analysis time and effort. We achieve this by performing semantic compression on the CAN traffic logs, rather than simple syntactic compression. Our compression method is lossless, thus preserving all information for later analysis. Besides all the above advantages, the compression ratio that we achieve is better than the compression ratio of state-of-the-art syntactic compression methods, such as gzip.

Nowadays, due to the constant change in the society's expectations regarding transportation, the main area of automotive development is the implementation of autonomous vehicle control functions. While with the gradual introducing and spreading of autonomous functions-even based on the previous experience-the decrease of the number of traffic accidents is expectable, parallel we have to prepare the emergence for the new types of accidents, in which autonomous vehicles will be involved. Needs of follow-up examination and reconstruction of such type accidents makes necessary the general use of the event/accident data recorders in road vehicles and application of the new methods and tools in accident reconstruction and examination of causes of accidents. Examining a road accident it is necessary to investigate the liability issues, as well, what requires new approaches and developing of a uniform practice.

Maps are constantly developing, also, the newly defined High Definition (HD) maps increase the map content remarkably. They are based on three-dimensional survey, like laser scanning, and then stored in a fully new structured way to be able to support modern-day vehicles. Beyond the traditional lane based map content, they contain information about the roads’ neighbourhood. The goal of these maps is twofold. Primarily, they store the connections where the vehicles can travel with the description of the road-environment. Secondly, they efficiently support the exact vehicle positioning. The paper demonstrates the first results of a pilot study in the creation of HD map of an urban and a rural environment. The applied data collection technology was the terrestrial laser scanning, where the obtained point cloud was evaluated. The data storage has been solved by an in-house developed information storage model with the ability to help in vehicle control processes.

Vehicles are not just a means of transportation anymore, but they are also moving entities that collect and transmit a great amount of local data, captured by sensors from the internals of the vehicle and from its environment as well. The consumers of this information include the passengers, especially the driver, and the environment, i.e. other vehicles, intelligent gates, centralized storage systems and data processing solutions. In order to maximize the value provided to the passengers, other vehicles and also the urban society, vehicle services utilize the local nature of the data. This paper discusses the VehicleICT platform, which is a proof of concept implementation, developed by a research institute and an automotive industrial partner.

Stuxnet is well-known in the IT security community. Its fame stems from the facts that it targeted a very specific industrial facility, it aimed at physical destruction, and it apparently accomplished its mission successfully. In addition to all these characteristics, IT security experts also appreciate its technical sophistication and the zero-day exploits that it used. Yet, we believe that the most important impact of Stuxnet in the long run is that it provides a blueprint for carrying out similar attacks in other embedded computing environments. To demonstrate this, we started experimenting with attacking cars in the same style as Stuxnet attacked uranium centrifuges. Our experiments show that it is relatively easy to perform dangerous modifications to the settings of different car equipment (e.g., the airbag or the ABS in the car) by simply infecting the mechanic’s PC or laptop that runs the diagnostic software used to manage those equipment in the car, and replacing the DLL responsible for communications between the diagnostic software and the car with a malicious DLL that implements man-in-the-middle type attacks (e.g., replay or modification of commands). Indeed, PCs and laptops in mechanics’ workshops are not well maintained, often connected to the Internet, and used for various purposes, not only for diagnostics on cars. So, while remote attacks via the Internet or wireless interfaces seems to be more exciting than attacking cars via the mechanic’s PC or laptop, the latter can in fact be much easier to carry out and more effective in practice. Note the similarity to Stuxnet, which infected PCs that run the software used to manage the PLCs that controlled the uranium centrifuges, and replaced the DLL that was responsible for the communications between the management software and the PLCs. More specifically, earlier this year, we carried out some research on a specific vehicle at the Department of Automotive Technologies of the Budapest University of Technology and Economics. We partially reverse engineered a widely used third-party car diagnostic software, and managed to identify and replace the DLL that it uses to communicate with a special diagnostic cable that attaches to the car’s OBD connector. Our replacement DLL implements man-in-the-middle attacks: we can log all communications between the diagnostic software and the car, as well as replay and modify messages. For this, we also had to partially reverse engineer the protocol used for communications, including figuring out how to decrypt encrypted messages and compute checksums. Finally, as a proof-of-concept, we managed to replay a message that switches off the airbag of the car without the diagnostic software noticing the misdeed.

The focus of the research and development activities in Intelligent Transportation Systems is to increase road traffic safety and improve transport efficiency. Telematics Systems can provide values on both fields, by providing real-time reliable vehicle specific information [1]. Road vehicle related information can be categorized into several types, but technical information provided through the electronic network of today's vehicles is becoming an increasingly important segment. Using the standard onboard diagnostics (OBD) is a popular solution nowadays for getting out technical data from the vehicle, but it has its limitations [2]. Professional systems rather use direct access to the vehicles CAN network to extract more detailed technical data out from the ECU communication [3]. Even automatic vehicle location (AVL) service providers have difficulties in differentiating between the possibilities and potentials of CAN bus and OBD. This article clearly identifies the differences between the solutions available on the market, by means of comparing the FMS CAN standard, the OBD standard and the Proprietary Vehicle CAN protocol. The comparison is not only theoretical, but it is based on different measurements and analysis. The measurement results demonstrate the benefits and limitations of these information sources. For the measurement we have used our VehicleICT [4] framework that provided the platform for storing and analyzing the data. Based on the provided comparison, the identified advantages and disadvantages of certain source types, we can select the proper solution for a specific requirement. One objective of this research activity is to clarify confusions about the relation of OBD and CAN from the Telematics Systems providers point of view. Besides that this research can be also considered as a pioneer work that demonstrates the capabilities of our VehicleICT framework.

In the following pages the authors would like to provide an introduction into the design, the architecture and functionality of today’s highly automated vehicle control systems. Thanks to the developments in electronics technology integrated into the automotive industry there has been a revolution in road vehicle technology in the past 20 years. In modern vehicles there are approximately 100 microcomputers today providing service to the driver by means of vehicle operation, comfort, assistance and safety. Electronics control almost every function inside the vehicle and there are attempts to harmonise vehicle-to-vehicle communication (V2V) and vehicle-to-infrastructure information exchange (V2I) as well. Advanced driver assistance systems (ADAS) take over more and more complex and complicated tasks from the driver like adaptive cruise control (ACC) or lane keeping assistance (LKA), to make driving even easier and safer. In the not too far future the public road vehicles will be able to drive without a driver. On one hand the technology improvements makes such functions feasible even on public roads, while on the other hand it raises new challenges to policy makers. Until that time there are a lot of tasks to be solved, several technical questions and maybe even more legal issues have to be answered.

The chapter presents a hardware-in-the-loop simulation environment, which is built in such a way that the simulator tends to the real vehicle functions as much as possible. The simulation system contains several components such as an Human Machine Interface (HMI), a high-accuracy validated simulation software operated on a PC and a visual system with real-time graphics. The simulator is equally suitable for educational and research purposes. All the vehicle engineer students use the simulator system during their curriculum, enabling the thorough understanding of modern vehicle functions, thus improving the competence of future generations of engineers. Moreover, the simulator system projects ahead the opportunity of new vehicle research that induces considerable additional scientific results.