High-dimensional traffic test scenario derivation for autonomous vehicles  

作　　者：Guoyu Zhang Aijing Kong Jian Sun Peng Hang 

机构地区：[1]Tongji University Ringgold standard institution,1239 Siping Road,Shanghai 200092,China

出　　处：《Chain》2024年第4期354-371,共18页链(英文)

摘　　要：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.

关 键 词：pan-scene architecture field theory particle filter intelligent driving scenario intelligent driving system 

分 类 号：O17[理学—数学]