The automated driving technology still faces many safety challenges in mixed traffic environments.Intersections are high-risk locations for autonomous vehicles(AVs).The aims of this study are pre-crash scenario generation and crash causation analysis of AVs.A pre-crash scenario method of roadway-traffic participant-critical event-precrash movement was developed.Thirty one pre-crash scenarios at intersections for AVs were generated using 470 crash reports involving AVs in California.Significant differences between the AV and conventional vehicle(CV)pre-crash scenarios were verified by a statistical analysis.A crash causation method is proposed based on the system control structure,which reveals the interaction relationship between AV crashes and roadway,traffic situation,environment,automated driving system,driver(tester),and vehicle.Nine crash causation patterns and causation chains of AV crashes in the rear-end and lane change scenarios were determined.The results indicate the following:AVs being rear-ended by CVs occurred with a frequency 4.03 times that of rear-ended CVs.The main reasons for rear-end scenarios were that the driver of a CV follows the lead vehicle too closely and insufficient decision-making of the automated driving system to decelerate first,and then stop or start.The main reasons for lane change scenarios were dangerous lane changes or overtaking of CVs,insufficient recognition of other vehicles'lane change intentions by the automated driving system,and unreasonable decision-making of deceleration and collision avoidance.This study promotes the application of scenario-based crash causation analysis methods.It can guide the construction of automated driving test scenarios,and provide a reference for the development and optimization of automated driving systems and improvement in intersection safety.
维普
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