匝道合流交通场景下自动驾驶汽车安全性测试评价方法

矿用无人运输车辆作业环境恶劣，存在大曲率弯道、坡道等非结构化道路明显特征，对无人化运输控制要求高。为改善PID等传统控制算法适应性问题，提高无人驾驶轨迹跟踪的车辆横纵向控制精度，提出一种纯跟踪与PID结合的多点预瞄横向控制、考虑模糊控制表参数拟合的纵向控制方法，减少控制参数的同时提高算法效果。根据传统控制算法设计基础控制器，结合基础算法优势进行横向与纵向控制算法设计，通过硬件在环仿真和实车测试验证算法的性能。试验结果表明，横向控制算法与斯坦利算法相比，车辆路径跟踪精度有明显改善，纵向控制方面，速度跟随误差<1 km/h，保证了车辆驾驶时的平稳性与舒适性。

Research on Safety Testing and Evaluation Methods for Autonomous Vehicles in Ramp Merging Traffic Scenarios

In order to promote the development of autonomous vehicle applications, conducting accurate and reliable safety testing and evaluation is essential. This paper proposes a safety evaluation method for autonomous vehicles tailored to high-speed ramp traffic scenarios using natural driving data. By analyzing the conflict characteristics in the confluence area, the models for calculating traffic conflict indicators such as TTC, PET and MSS are established to determine the safety evaluation indicators. The fuzzy clustering of natural driving indicator data is used to obtain the threshold ranges for these indicators. The autonomous vehicle simulation test has been built. The importance criterion weight distribution method based on interlayer correlation and the gray correlation scoring model are applied. The comprehensive evaluation scores regarding the safety of autonomous vehicles are calculated under different control algorithms. The results show a distinct correlation in the distribution of safety indices between the test vehicle''s drivingbehavior and ideal driving behavior. By calculating the overall correlation degree, the scores can directly reflect the comprehensive safety performance of different autonomous driving systems.