自动驾驶系统逻辑场景全覆盖测试用例生成方法OA

The scenario-based testing method is the mainstream means to verify the safety of the automated driv-ing system(ADS).However,the logical scenario uses the form of parameter space to describe the scenario.It is dif-ficult for the third-party detection organizations to use the same test case to ensure the test fairness and test cover-age when the performance of the system under test is different.For this reason,this paper proposes a full coverage test cases generating method for ADS in logical scenario based on the test case representativeness.First,a systemat-ic full coverage testing framework tailored for ADS is established.Subsequently,a quantitative evaluation method is introduced to assess the representativeness of test cases by jointly analyzing naturalistic driving probability distri-butions and hazardous event characteristics.Finally,an optimization calculation method for achieving full coverage of the differentiated sample combination space is developed,based on a heat-driven hierarchical greedy algorithm integrated with a genetic algorithm,enabling the efficient acquisition of representative parameter combinations that achieve full coverage of the logical scenario parameter space.The proposed approach is empirically validated using a lead-vehicle cut-in scenario.The results indicate that the proposed method achieves a logical scenario parameter space coverage rate of 100%and a boundary fitting error of 0.08,both of which significantly outperform current mainstream approaches,including the Monte Carlo method(coverage rate:84.3%,fitting error:0.19)and combinat-orial testing(coverage rate:86.5%,fitting error:0.14).These findings demonstrate the method's potential to effect-ively support testing organizations in developing a fair and efficient scenario generation framework.