High-Dimensional Traffic Test Scenario Derivation for Autonomous Vehicles

To enhance the testing efficiency of autonomous vehicles, it is essential to derive intelligent traffic test scenarios. Current methods face limitations such as reliance on subjective analysis and neglect of inter-element correlations. This study introduces Kalman particle filtering theory for high-dimensional traffic scenario derivation. By analyzing comprehensive energy fields in normalized scenes with various elements, we define benchmark scenes using field energy theory. Multi-level research is conducted on processing high-dimensional spatial element data, proposing a normative paradigm for weight allocation among scene elements. We perform generalized derivation by extending hierarchical elements based on offset values, meeting functional verification requirements. Simulation experiments comparing risk event detection, decision-making, and feedback behavior between the proposed method and actual driving data show a steering matching index of 0.92, a longitudinal speed matching index of 0.96, and an root mean squared error (RMSE) mean value of 0.06.