基于FES-GALMBP模型的低速自动驾驶车辆服务质量测试评价OA北大核心

To solve the problem of service quality testing and evaluation for low-speed automated vehicles with low accuracy of traditional automated driving function evaluation methods,the V2I-based simulation test platform for autonomous vehicles in the loop was designed,and the FES-GALMBP evaluation algorithm was proposed.The evaluation indicators for low-speed autonomous vehicle service quality were determined by the HotSpot association rule method.The indicator weights were optimized through AHP-CRITIC subjective-objective combined weighting,and the multi-level fuzzy comprehensive evaluation model with incorporating criterion layer,standard layer and indicator layer weight calculations was constructed.The fuzzy expert system(FES)was utilized to evaluate the test sample set,and the evaluation results were used as training data for the FES-GALMBP neural network model.The Prescan/MATLAB was used to complete co-simulations and real-vehicle tests in low-speed autonomous bus scenarios.The results show that by the proposed FES-GALMBP model,the accuracy rates of 94％ for operational quality and 80％for operational safety are achieved,which are significantly higher than those by the traditional BP neural network model with 59％ and 53％,respectively.The AUC values for all prediction categories of the proposed model are higher than those of the traditional BP model,which illuminates that the novel model has superior classification performance.