Zhang Yulong, Wang Pengwei, Cui Kaichen, Zhou Hengheng, Yang Jinshan, Kong Xiangcun
School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255022, China.
Sensors (Basel). 2023 Sep 28;23(19):8151. doi: 10.3390/s23198151.
To meet the real-time path planning requirements of intelligent vehicles in dynamic traffic scenarios, a path planning and evaluation method is proposed in this paper. Firstly, based on the B-spline algorithm and four-stage lane-changing theory, an obstacle avoidance path planning algorithm framework is constructed. Then, to obtain the optimal real-time path, a comprehensive real-time path evaluation mechanism that includes path safety, smoothness, and comfort is established. Finally, to verify the proposed approach, co-simulation and real vehicle testing are conducted. In the dynamic obstacle avoidance scenario simulation, the lateral acceleration, yaw angle, yaw rate, and roll angle fluctuation ranges of the ego-vehicle are ±2.39 m/s, ±13.31°, ±13.26°/s, and ±0.938°, respectively. The results show that the proposed algorithm can generate real-time, available obstacle avoidance paths. And the proposed evaluation mechanism can find the optimal path for the current scenario.
为满足智能车辆在动态交通场景下的实时路径规划需求，本文提出了一种路径规划与评估方法。首先，基于B样条算法和四阶段变道理论，构建了一种避障路径规划算法框架。然后，为获得最优实时路径，建立了一个包括路径安全性、平滑性和舒适性的综合实时路径评估机制。最后，为验证所提方法，进行了联合仿真和实车测试。在动态避障场景仿真中，自车的横向加速度、偏航角、偏航率和侧倾角波动范围分别为±2.39 m/s、±13.31°、±13.26°/s和±0.938°。结果表明，所提算法能够生成实时、可用的避障路径。并且所提评估机制能够为当前场景找到最优路径。
