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.

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.

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.