基于Frenet坐标系的自动驾驶轨迹规划与优化算法

Trajectory planning and optimization algorithm for automated driving based on Frenet coordinate system

针对公路场景下作速度保持的自动驾驶汽车实时轨迹规划问题,提出一种基于Frenet坐标系的优化轨迹规划算法.首先,利用Frenet坐标系将车辆运动解耦,构建无约束横向/纵向独立积分系统;然后,根据初始配置和可内嵌到行为层的目标配置,通过采样生成有限的4次、5次多项式候选轨迹集合;最后,利用以高斯卷积、加速度变化率为核心的安全性和舒适性评价指标构建损失函数,评价轨迹成本,并结合曲率、加速度检查,选择能够最小化损失的最优解.结果表明,该算法能满足公路型场景的规划需求,车辆运动轨迹平滑、舒适、安全性更高.

Aiming at the real-time trajectory planning of autonomous vehicles with velocity keeping under highway scenarios, an optimal trajectory planning algorithm based on the Frenet coordinate system is proposed. Firstly, the vehicle motion is decoupled using the Frenet coordinate system to construct lateral/longitudinal independent integrator systems. Secondly, according to the initial configuration and the target configuration which can be embedded into the behavior layer, a limited set of quartic and quintic polynomial candidate trajectories are generated by sampling. Thirdly,the loss function is build using the safety and comfort evaluation index with Gaussian convolution and acceleration deriative as the core to evaluate the trajectory cost. From the trajectories checked by curvature and acceleration, the one that can minimize the loss is selected as the optimal solution. The results show that the proposed algorithm can meet the requirements of road-type scene, the motion trajectory is smooth, comfortable and safer.