Validating SuperHuman Automated Driving Performance
Zlatan Ajanovic (Virtual Vehicle Research GmbH)
Matthijs Klomp (Volvo)
Bakir Lacevic (University of Sarajevo)
Barys Shyrokau (TU Delft - Intelligent Vehicles)
Paolo Pretto (Virtual Vehicle Research GmbH)
Hassaan Islam (Virtual Vehicle Research GmbH)
Georg Stettinger (Virtual Vehicle Research GmbH)
Martin Horn (Graz University of Technology)
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Abstract
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.