Velocity planning serves as an important issue in motion planning for autonomous vehicles. The presented paper proposes a novel velocity planning method with minimum moving time on the basis of path curvature which is accomplished in three steps. First, the assigned path is divided into some elementary parts based on the path curvature. Second, the velocity planning is transformed into an unconstrained optimization problem by assuming the velocity of vehicle to be a specific cubic polynomial on every elementary part to avoid a sudden acceleration in path switching. Finally, we use a modified projection particle swarm optimization (PPSO) algorithm to obtain the time-optimal velocity profile. The proposed method can generate a smooth time-optimal velocity profile while considering all possible relevant constraints. Three examples are provided on different types of path to demonstrate that the final velocity profile is efficient to avoid the sudden acceleration change. Furthermore, the modified PPSO algorithm in this paper is used to solve the optimization problem with high dimensional variables when its upper bound is known, which can not be achieved by the general PPSO algorithm.

速度计划是自动驾驶汽车运动计划中的重要问题。提出了一种基于路径曲率的最小移动时间的速度规划方法，该方法分三个步骤完成。首先，根据路径曲率将分配的路径分为一些基本部分。其次，通过将车辆的速度假定为每个基本部分上的特定三次多项式来避免路径切换中的突然加速，将速度规划转化为无约束的优化问题。最后，我们使用改进的投影粒子群算法（PPSO）算法来获取时间最佳速度曲线。所提出的方法可以在考虑所有可能的相关约束的同时生成平滑的时间最优速度曲线。在不同类型的路径上提供了三个示例，以证明最终速度曲线可有效避免突然的加速度变化。此外，本文提出的改进的PPSO算法被用于解决高维变量的优化问题，当它的上限已知时，这是一般PPSO算法无法实现的。