无人驾驶车辆局部路径规划的时间一致性与鲁棒性研究

在无人驾驶系统中,局部规划在跟踪全局路径的同时完成避障,提高了规划系统在动态未知环境中的工作能力.避障分析的有效性是局部规划最重要的功能之一.然而在仿真和实车测试中发现,广泛使用的基于优化求解的局部规划算法无法在不依赖全局精确定位时保证规划结果满足时间一致性要求.时间不一致将导致车辆的实际行驶路线偏离初始规划结果,造成避障分析失效.本文设计了基于前向预测的局部路径规划算法,在不依赖全局精确定位的前提下保证规划结果的时间一致性.除了时间一致性问题外,跟踪控制误差也是导致规划结果避障分析失效的主要原因之一.现有研究大多通过膨胀障碍物体现误差的影响,然而这种方法无法避免车辆驶入膨胀危险区域而停车.本算法在路径生成过程中增加误差影响,用通行区域代替原有不具有宽度的规划路径进行避障分析,既可以解决误差导致的避障失效,又避免出现膨胀障碍物带来的问题.通过V-Rep软件与实车规划程序进行联合仿真,在能够体现时间一致性影响的典型场景中对本算法与基于最优化曲线生成的局部路径规划算法进行比较, 验证了该算法具有更好的安全分析有效性.应用本算法的北京理工大学无人驾驶平台参加了2013年智能车未来挑战赛,在无人干预的情况下顺利完成 18公里城郊赛段和5公里城市赛段行驶,展现了良好的避障能力.

Research on Temporal Consistency and Robustness in Local Planning of Intelligent Vehicles

Local planner can improve the capacity of planning system for intelligent vehicles by avoiding obstacles while tracking the reference path. Safety check is the one of the fundamental functions of a local planner. However, revealed by simulation and experiments, the widely used optimization-based local planning methods are unlikely to maintain temporal consistency without any accurate global positioning information. Temporal inconsistency will result in the deviation of the vehicle''s actual trajectory from the original planned results, which will finally make the safety check invalid. This paper presents a forward prediction-based local planning algorithm which holds temporal consistency in the results without requiring any accurate global positioning information. Besides the temporal consistency issue, controlling error is another reason for safety check failure. Most current researches take error into consideration by enlarging the size of obstacles. Such methods are unable to prevent the vehicle from entering the dilated obstacle areas. In this paper, controlling error is introduced in the generation of the local paths. The traditional path with no width is replaced by a strip of path here. Based on the simulation results of the V-Rep virtual reality software, the forward prediction-based method features better safety check ability as compared with the optimization-based local planning methods. The proposed algorithm was applied to the intelligent vehicle by Beijing Institute of Technology which participated in The Future Challenge 2013. The vehicle succeeded to finish the event without human operation.