In order to solve the local path planning of self-driving car in the structured road environment, an improved path planning algorithm named Regional-Sampling RRT (RS-RRT) algorithm was proposed for obstacle avoidance conditions. Gaussian distribution sampling and local biasing sampling were integrated to improve the search efficiency in the sampling phase. In the expansion phase, considering the actual size of the vehicle and obstacles, combined with the goal of safety and comfort, the separating axis theorem (SAT) method and vehicle dynamics were used to detect the collision among vehicle and surrounding obstacles in real time. In the post-processing stage, the driver’s driving consensus and path smoothing algorithm were combined to correct the planning path. In order to track the generated path, the MPC tracking algorithm was designed based on the Four-Wheel-Independent Electric Vehicle (FWIEV) model. The co-simulation software platform of CarSim and MATLAB/Simulink was employed to verify the effectiveness and feasibility of the path planning and tracking algorithm. The results show that compared with basic RRT and Goal-biasing RRT, the proposed RS-RRT algorithm has advantages in terms of number of nodes, path length and running time. The generated path can meet the FWIEV dynamics and path tracking requirements.

为了解决结构化道路环境下无人驾驶汽车的局部路径规划问题，针对避障条件，提出了一种改进的路径规划算法，称为区域采样RRT（RS-RRT）算法。高斯分布采样和局部偏差采样相结合，以提高采样阶段的搜索效率。在扩展阶段，考虑到车辆的实际大小和障碍物，并结合安全性和舒适性的目标，采用分离轴定理（SAT）方法和车辆动力学来实时检测车辆与周围障碍物之间的碰撞。在后处理阶段，将驾驶员的驾驶共识和路径平滑算法结合起来以校正规划路径。为了跟踪生成的路径，基于四轮独立电动汽车（FWIEV）模型设计了MPC跟踪算法。利用CarSim和MATLAB / Simulink的协同仿真软件平台，验证了路径规划和跟踪算法的有效性和可行性。结果表明，与基本RRT和目标偏向RRT相比，提出的RS-RRT算法在节点数，路径长度和运行时间方面具有优势。生成的路径可以满足FWIEV动态和路径跟踪要求。提出的RS-RRT算法在节点数，路径长度和运行时间方面均具有优势。生成的路径可以满足FWIEV动态和路径跟踪要求。提出的RS-RRT算法在节点数，路径长度和运行时间方面均具有优势。生成的路径可以满足FWIEV动态和路径跟踪要求。