基于运动微分约束的无人车辆纵横向协同规划算法的研究

为了满足在动态环境中快速行驶的要求，现有无人车辆普遍采用在传统规划系统的两层结构（路径规划-路径跟踪）之间增加局部规划的方法，通过在路径跟踪的同时进行避障来减少耗时的全局路径重规划. 本文针对这种三层结构规划系统存在的问题，提出基于运动微分约束的纵横向协同规划算法，在真实环境中实现速度不超过40km/h的无人驾驶. 根据车辆的实时运动状态，用高阶多项式模型在预瞄距离内对可行驶曲线进行建模，不仅使行驶过程中的转向平稳，而且在较高速时仍具有良好的路径跟踪能力. 由横向规划提供横向安全性的同时，在动力学约束的速度容许空间中进行纵向规划，实现平顺的加速与制动，并保证了纵向安全性和侧向稳定性. 该算法根据实时的局部环境自动决定纵横向期望运动参数，不需要人为设定行驶模式或调整参数. 采用该算法的无人驾驶平台在2011年和2012年智能车未来挑战赛的真实交通环境中，用统一的程序框架顺利完成全程的无人驾驶.

Research on Differential Constraints-based Planning Algorithm for Autonomous-driving Vehicles

For better timing performance, existing autonomous driving platforms generally introduce local planner into the conventional two-layer path panner scheme (path planning path following) to reduce the requirements for costly global replanning by avoiding collision with obstacles while keeping tracking the desired path. This paper presents an improved three-layer planning algorithm for fully autonomous driving in real scenarios at a speed up to 40km/h. Compared with general algorithms, differential constraints are taken into account in the local planner to improve the elegance in steering control and provide a better tracking ability at high speed. Longitudinal planning based on speed profile under dynamic constraints is involved in the planner as well so as to provide the smoothness safety and stability in driving. Desired motion commands are generated based on the local environment without manually tuned parameters, which is helpful for a general-purpose autonomous driving system. The algorithm was implemented on the BIT self-driving platform in 2011 and 2012 Intelligent Vehicle Future Challenge.