Generation Framework Based on Hierarchical Classification for Testing of Automated Vehicles

The generation of unknown unsafe scenarios is crucial for the verification and validation of automated vehicles. However, the complexity of traffic and the sensitivity of testing costs pose significant challenges in reducing the unknown unsafe region. To address this problem, a framework is proposed for generating unknown unsafe scenarios based on hierarchical classification. Firstly, the critical scenario library is obtained using an evolving simulation system. Secondly, the scenarios in the scenario library are hierarchically classified based on the maneuver, position, and motion of the vehicles, resulting in abstract scenarios. Finally, a concretization method is employed to search for potential unsafe scenarios within these abstract scenarios. This methodology was applied to the testing of the Stackelberg algorithm. The evolving system generated 6142 scenarios. These were then classified using the abstraction method, yielding 35 abstract scenarios. For the abstract scenario with the highest hazard rate of 19.47%, the concretization method was used to design logical scenarios. Based on an optimization algorithm, 443 potential concrete unsafe scenarios were identified.