Closed-loop validation of autonomous vehicles is an open problem, significantly influencing development and adoption of this technology. The main contribution of this paper is a novel approach to reproducible, scenario-based validation that decouples the problem into several sub-problems, while avoiding to brake the crucial couplings. First, a realistic scenario is generated from the real urban traffic. Second, human participants, drive in a virtual scenario (in a driving simulator), based on the real traffic. Third, human and automated driving trajectories are reproduced and compared in the real vehicle on an empty track without traffic. Thus, benefits of automation with respect to safety, efficiency and comfort can be clearly benchmarked in a reproducible manner. Presented approach is used to benchmark performance of SBOMP planner in one scenario and validate SuperHuman driving performance.

This paper illustrates a comparison of different haptic feedback control strategies; primarily focusing on open and closed-loop methods for a Force-Feedback Steer-by-Wire system. Due to shortcomings caused by the feedback motor impedance in the open loop architecture, the tracking performance is deteriorated. Consequently it is shown that the closed-loop solutions provide an improved response within the desired steering excitation range. The closed-loop possibilities, torque and position control, are designed and objectively compared in terms of performance and stability. The controller objectives are inertia compensation and reference tracking. For a given reference, the stability constraint between the controller gains responsible for the two objectives is contrasting in both the methods. Higher bandwidth is achieved for torque controller, whereas the driver arm inertia limits the position control performance. The linear system analysis is supported by the experimental results.