Virtual Verification of Decision Making and Motion Planning Functionalities for Automated Vehicles in Urban Edge Case Scenarios

Despite recent advancements in Automated Driving Systems (ADS), the deployment of such systems in dense urban environments still faces a challenging problem: in comparison to motorway or rural driving, urban environments contain a significantly greater number of traffic participants. This makes it difficult to verify the Safety Of The Intended Functionality (SOTIF) across the entire Operational Design Domain (ODD). One approach to solve this problem is to virtually evaluate and verify the safety of the ADS using simulation tools. Whereas traditionally simulated verification has attempted to replicate normal driving conditions, it is possible to achieve superior safety performance by exposing the ADS to more high-risk scenarios than it would otherwise see in the real world. This paper presents the virtual verification process for decision making and motion planning functionalities in urban high-risk edge case scenarios. At the outset of this study a novel data-driven methodology is used to define numerous urban driving edge cases based on real-world road traffic collisions and near-misses, rather than merely everyday driving. These edge cases are then translated into driving scenarios based on the OpenSCENARIO 1.0 standard and then simulated in batches using the open-source CARLA simulator. The simulator is connected to a decision making and motion planning functionality responsible for controlling the state of the ego-vehicle. The efficacy of this edge-case-based virtual verification pipeline is demonstrated with practical examples where the performance of the functionality in urban driving edge cases is iteratively improved to meet the safety requirements.