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A Practical Sampling-based Motion Planning Method for Autonomous Driving in Unstructured Environments

机译：基于实际的采样运动规划方法，用于非结构化环境中的自主驾驶

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Motion planning is a fundamental technique for autonomous driving that guides the ego vehicle to the destination. Demands of driving safety and real-time calculation make it tend to adopt a layered strategy in the motion planning. This paper proposes an efficient method for the reference trajectory generation at a global level in unstructured driving environments. First, sampling-based path construction and optimization are applied to incrementally search for a feasible spatial reference path. Then, a trapezoidal speed profile, further smoothed by a cubic B-spline, is carefully designed along the path considering speed and acceleration limits. Finally, a simplified tracking method is implemented along the speed-path-coupled reference to obtain a feasible control sequence and further smooth the trajectory. Simulation results illustrate the effectiveness of the proposed method that it can efficiently obtain a collision-free trajectory, including waypoints information as well as speed and steering inputs profiles.
机译：运动规划是一种用于自动驾驶的基本技术，将自动驾驶引导到目的地。驾驶安全性和实时计算的需求使得它倾向于在运动规划中采用分层策略。本文提出了一种在非结构化驾驶环境中全球层面的参考轨迹生成的有效方法。首先，应用基于采样的路径结构和优化来逐步搜索可行的空间参考路径。然后，沿着考虑速度和加速度限制的路径小心地设计梯形速度轮廓，进一步平滑立方B样条。最后，沿着速度路径耦合的参考来实现简化的跟踪方法，以获得可行的控制序列并进一步平滑轨迹。仿真结果说明了所提出的方法的有效性，即它可以有效地获得无碰撞轨迹，包括航点信息以及速度和转向输入轮廓。

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