ODD-based Query-time Scenario Mutation Framework for Autonomous Driving Scenario databases

Yun Tang, Dhanush Raj, Xingyu Zhao, Xizhe Zhang, Antonio Anastasio Bruto da Costa, Siddartha Khastgir, Paul A. Jennings

Abstract: Large-scale scenario databases may contain hundreds of thousands of scenarios for the verification and validation (V&V) of autonomous vehicles (AV). Scenarios in the database are often labelled with semantic Operational Design Domain (ODD) tags (e.g., WeatherRainy, RoadTypeHighway and ActorTypeTruck) to be queried via exact tag matching. Such a scenario database design has two major limitations, i.e. combinatorial scenario generation inevitably leads to many redundant scenarios, and each ODD query matches only a small number of scenarios in the database (0.2% in our case study), rendering most of the database wealth wasted. We propose a novel scenario database design and the first ODD-based query-time scenario mutation framework to address the limitations. Our case study results show that the proposed framework has the potential to fully utilize all the database scenarios at query time while eliminating scenario redundancy in the database (in our case study, given the same ODD query, the number of final matched scenarios increased by 36 times, diversity increased by 99 times, and scenario database utilization rate increased from 0.2% to 36%).