考虑车辆运动约束的最优避障轨迹规划算法

Optimal Obstacle Avoidance Trajectory Planning Algorithm Considering Vehicle Motion Constraints

无人驾驶车辆进行障碍物规避时,须考虑车辆的运动特性以保证避障过程的安全性、舒适性、操纵稳定性等。本文中提出了一种在车辆运动约束下的避障轨迹规划算法。该算法结合障碍物的运动状态和位姿信息,在局部环境地图中对超越障碍物的期望位置进行局部采样组成离散终端状态点集,将复杂道路环境中的避障轨迹搜索问题转换为自车与状态点集之间的轨迹拟合和寻优问题。轨迹的拟合通过基于车辆侧向动力学模型的Bézier曲线规划器实现,而寻优过程则考虑到了车辆进行轨迹跟随过程中的行驶平顺性和操纵稳定性。通过与常规State Lattice算法和MPC算法在多种测试环境中进行避障效果的对比,结果表明本文中提出的规划方法在测试场景中能够使车辆安全、合理地规避障碍,同时在轨迹平顺性、操纵稳定性等方面有较好的表现。

The motion characteristics of the autonomous vehicle should be taken into account to ensure safety,comfort and stability when avoiding obstacles.An obstacle avoidance trajectory planning algorithm with the constraints of vehicle motion is proposed in this paper.Firstly,combining with the motion state and position information of obstacles,the algorithm derives the derivative status region where the vehicle can safely avoid obstacles from the local environment map.Then,the terminal state points are sampled in the derivative status region to form a discrete terminal state point set.The obstacle avoidance trajectory search problem in complex road environment is transformed into the problem of trajectory fitting and optimization between the vehicle and the state point set.Trajectory fitting is realized by Bézier curve planner based on vehicle lateral dynamics model while the optimization process takes into consideration of driving smoothness and operation stability in the process of vehicle trajectory following.By comparing the obstacle avoidance effect in multiple scenarios with the conventional State Lattice algorithm and MPC algorithm,the results show that the proposed algorithm can make the vehicle avoid obstacles safely and reasonably in the test scenarios,and has better performance in the aspects of trajectory smoothness and vehicle handling stability.