Danger Field Identification Method for Evaluation of Autonomous Driving Logic Scenarios
It is crucial to evaluate the comprehensiveness of safety testing for autonomous driving.Therefore,in this study,evaluation objectives of logical scenario evaluation including comprehensiveness,accuracy,and visibility are proposed.Furthermore,a method based on test results in discrete specific scenarios to identify the danger field in logical scenarios,including danger field modes,distributions,and proportions,is proposed to comprehensively evaluate the safety of the system under test(SUT)at the logical scenario level.First,specific dangerous scenarios were clustered by the Mean Shift algorithm to discover different categories of dangerous scenarios.Second,a Decision Tree by memorization feature selection is proposed to partition the boundary of each danger field mode.Third,the partitioning path was analyzed to automatically calculate the proportion of the danger field.To verify the proposed danger field identification method,it was compared to the baseline on the multimodal test function.The results show that the proposed method is better than the baseline method in the calculation accuracy of the danger field modes,distributions,and proportions.Furthermore,application experiments were conducted on test functions and logical scenario.On the test function,danger field identification was carried out based on different optimization algorithms,verifying the universality of the proposed danger field identification method.The search efficiencies of different optimization methods were compared based on the identification results.In the logical scenarios,three danger field modes with a proportion of 44％and corresponding spatial distributions are identified by the proposed identification method.Based on the analysis of two typical dangerous scenarios,four improvement requirements are proposed for the SUT.The research results show that the proposed method of danger field identification can effectively identify the danger field in logical scenarios of autonomous driving,and comprehensively and accurately evaluate the safety of the SUT at the logical scenario level.
automotive engineeringlogical scenario evaluationdanger field identificationsimu-lation testingsafety evaluation
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