事故接管场景下L3自动驾驶换道轨迹的评价和分类

为研究L3自动驾驶事故场景下人工接管后换道轨迹的评价和分类问题，通过驾驶模拟实验采集换道轨迹数据；从舒适性、高效性、生态性、安全性四个方面选取9个评价指标；采用熵权TOPSIS模型对换道轨迹进行评价并完成标签标定；用标定后的数据训练得到SVM分类器模型，并将其应用于换道轨迹的分类中，该模型在测试集的平均准确率为79.55%，平均精确率为79.52%，平均召回率为79.51%，平均F1值为77.43%。结果表明：应用熵权TOPSIS模型得到的评分最高的换道轨迹在舒适性、高效性、生态性和安全性上综合表现优秀；SVM分类器能以较为稳定的准确率完成换道轨迹的分类。得到的最优换道轨迹可为驾驶员的换道提供指导，也可为自动驾驶车辆的轨迹遵循提供参考。

Evaluation and classification of L3 automatic driving lane-changing trajectory in accident takeover scenarios

In order to study the evaluation and classification of lane-changing trajectory in L3 autonomous driving accident scenarios, the lane-changing trajectory data was collected through driving simulation experiments; Nine evaluation indicators are selected from the four aspects of comfort, efficiency, ecology, and safety; Use the entropy weight TOPSIS model to evaluate the lane change trajectory and complete the label calibration; Train the calibrated data to obtain the SVM classifier model and apply it to the classification of lane-changing trajectories. The average accuracy of the model in the test set is 79.55%, the average accuracy is 79.52%, and the average recall is 79.51%, the average F1 value is 77.43%. The results show that the highest-scoring lane-changing trajectory obtained by applying the entropy weight TOPSIS model has a comprehensive performance in comfort, efficiency, ecology and safety; The SVM classifier can complete the classification of the lane-changing trajectory with a relatively stable accuracy. The obtained optimal lane-changing trajectory can provide guidance for the driver to change lanes, and can also provide a reference for the trajectory following of the autonomous vehicle.