智能汽车采样区域优化的避障路径规划

Obstacle avoidance path planning for intelligent vehicle sampling area optimization

针对结构化道路下智能汽车避障路径规划问题,提出一种采样区域优化的避障路径规划方法。充分考虑道路环境和障碍车辆信息,建立障碍车辆膨胀椭圆层模型对道路环境进行风险划分;将避障过程分为换道避障、换道后直行、返回全局路径参考线3个阶段,在每个阶段中,通过膨胀椭圆层模型选取低风险采样点生成候选路径;考虑路径舒适性和全局路径追踪能力,设计舒适性及偏移代价函数,结合膨胀椭圆层模型建立安全性代价函数,选取最优路径。为检验方法的有效性,通过构建直道和弯道场景,进行避障仿真和实车验证。研究结果表明,在不同场景下,所提出的方法能有效避免与静态、动态障碍车辆发生碰撞,高效规划出安全、平滑的行驶路径。

Aiming at obstacle avoidance path planning for intelligent vehicles on structured roads,this paper proposes an obstacle avoidance path planning method based on sampling area optimization.Fully considering the road environment and obstacle vehicle information,it establishes an obstacle vehicle expansion ellipse layer model to divide the risk of the road environment.The obstacle avoidance process is divided into three stages:changing lanes to avoid obstacles,going straight after changing lanes,and returning to the global reference route.In each stage,low-risk sampling points are selected to generate candidate paths through the expansion elliptic layer model.Considering path comfort and global path tracking abilities,this paper designs the comfort and offset cost functions,and combines the expansion ellipse layer model to establish the safety cost function and select the optimal path.In order to test the effectiveness of the method,the obstacle avoidance simulation and real vehicle verification are carried out by constructing straight roads and curve scenes.The research results show that under different scenarios,the proposed method can effectively avoid collision with static and dynamic obstacle vehicles,and efficiently plan safe and smooth driving paths.