B样条曲线下的MPC轨迹重规划算法

MPC Trajectory Re-Planning Algorithm Under B-Spline Curve

为加强无人驾驶车辆在复杂环境道路上行驶的平滑性,减少车辆的抖振,提高乘坐舒适性,提出了一种基于B样条曲线的MPC轨迹重规划算法。该算法将B样条曲线与MPC轨迹重规划算法进行有效结合,利用B样条曲线局部性和凸包性的优势,对重规划算法规划出的离散点进行拟合,提高规划路径的平滑性。基于Matlab/CarSim进行联合仿真,对建立的轨迹重规划模型及控制模型进行双移线工况下的仿真验证。结果表明:相比于不加平滑算法的规划轨迹,双移线工况下,多项式拟合曲线规划出的路径侧向最大加速度和横摆角速度分别降低了56. 01%和60. 52%,B样条曲线降低了77. 08%和68. 55%,验证了算法的正确性和有效性。

To enhance the smoothness of autonomous vehicles on complex roads,reduce the buffeting of vehicles and improve passenger comfort,a new MPC trajectory re-planning algorithm based on Bspline curve is proposed. This algorithm effectively combined the B-spline curve and MPC trajectory re-planning algorithm. By using the advantages of locality and convexity of B-spline curve,the discrete points of re-planning algorithm were fitted to improve the smoothness ofthe planning path. The established trajectory re-planning model and control model were verified under the conditions of double line change using the Matlab/CarSim software. The results show that compared with the planned trajectory without smoothing algorithm,under the double line change condition,the lateral maximum acceleration and yaw angular velocity of the path planned by polynomial fitting curve were reduced by56. 01% and 60. 52%,respectively,and the B-spline curve were reduced by 77. 08% and 68. 55%,which verified the correctness and validity of this algorithm.