Construction Test Set and Risk Assessment of Cut-in Scenarios for Autonomous Vehicles under Highway Congestion Conditions
To assess the safety risks of autonomous vehicles during cut-in scenarios on congested Chinese highways,64 cut-in samples were extracted from a natural driving dataset.Employing a six-level model and correlation analysis,the static and dynamic factors of the scenarios were defined.Subsequently,1 000 test cases were randomly generated through sampling,and a safety assessment index system was established to analyze the safety of vehicle operations.Lastly,the random forest algorithm was applied to identify the key factors triggering risks.Results indicate that risk scenarios account for 5.3％of the total,with longitudinal relative velocity identified as the crucial factor.Under congested conditions,a high-risk cut-in scenario is formed when the speed of surrounding vehicles is 23％lower than that of autonomous vehicles,this indicator serves as a crucial predictive measure for identifying collision risks in congested cut-in scenarios for autonomous vehicles and may be applied in determining liability of accident in such scenarios.
万方数据
维普
