Motion planning is one of the key modules in autonomous driving systems to generate trajectories for self-driving vehicles to follow. A common motion planning approach is to generate trajectories within semantic safe corridors. The trajectories are generated by optimizing parametric curves (\textit{e.g.} Bezier curves) according to an objective function. To guarantee safety, the curves are required to satisfy the convex hull property, and be contained within the safety corridors. The convex hull property however does not necessary hold for time-dependent corridors, and depends on the shape of corridors. The existing approaches only support simple shape corridors, which is restrictive in real-world, complex scenarios. In this paper, we provide a sufficient condition for general convex, spatio-temporal corridors with theoretical proof of guaranteed convex hull property. The theorem allows for using more complicated shapes to generate spatio-temporal corridors and minimizing the uncovered search space to $O(\frac{1}{n^2})$ compared to $O(1)$ of trapezoidal corridors, which can improve the optimality of the solution. Simulation results show that using general convex corridors yields less harsh brakes, hence improving the overall smoothness of the resulting trajectories.
翻译:运动规划是自动驾驶系统的关键模块之一,以产生自驾驶车辆的轨迹。一个共同的运动规划方法是在语义安全走廊内产生轨迹。轨迹是根据客观功能优化参数曲线(\ textit{e.g.} Bezier曲线)产生的。为了保证安全,曲线必须用来满足螺旋船体属性,并被控制在安全走廊内。但对于依赖时间的走廊来说,锥形船体属性并不需要保持,而是取决于走廊的形状。现有的方法只支持在现实世界中限制性的简单形状走廊,复杂的情景。在本文中,我们为一般二次曲线、黑洞时空走廊提供了充分的条件,并有保证的矩形结构属性的理论证明。为了保证安全,曲线需要使用更复杂的形状来生成孔状-时空走廊,并将未发现的搜索空间减少到$O(\ max{{{{{n} 取决于走廊的形状。现有方法只支持在现实世界中具有限制性的简单形状走廊。在本文中,我们为一般二次曲线- 提供了足够条件, 的走廊提供了足够条件,,, 以便用最慢的轨道 来改善总体的制式走廊, 能够改善整个制成。