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.

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.

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.