Criticality Assessment Model for Intelligent Vehicle Test Scenario Based on Interactive Field Feature and Hypergraph Learning

Scenario-based testing is an important part in intelligent vehicle (IV) development. The data volume of collected test scenarios is extremely large, and directly using all collected scenarios to test IVs will lead to extremely low testing efficiency. To solve this problem, a criticality assessment model (CAM) for IV test scenario based on interactive field feature (IFF) and hypergraph learning is proposed to quantify the test scenario criticality to improve the test efficiency. The IFF is constructed based on the potential field-based method to integrally consider the multidimensional coupling of scenario elements. In addition, the interaction between the fields generated by the ego vehicle and the driving environment is modeled based on Delaunay triangulation discretization method to accurately quantify the driving environment risk to the ego vehicle. The node and hyperedge of the hypergraph are used to model the individual dynamic evolution and group interaction characteristics of vehicles, respectively. Subsequently, a hypergraph learning network is constructed to extract features from the built hypergraph, IFF and traffic elements. Finally, the effectiveness, reasonableness and accuracy validation experiments are designed to validate the proposed CAM. Ablation experiment results show that the proposed IFF and hypergraph learning network enhance the CAM accuracy. The reasonableness validation results show that the proposed CAM can better find critical test scenarios than time-to-collision and time-head-way methods. The accuracy of the proposed CAM is compared through four real validation scenarios in the proving ground. The comparison results show that the proposed CAM can accurately output the quantified test scenario criticality.