Safety-guaranteed motion planning is critical for self-driving cars to generate collision-free trajectories. A layered motion planning approach with decoupled path and speed planning is widely used for this purpose. This approach is prone to be suboptimal in the presence of dynamic obstacles. Spatial-temporal approaches deal with path planning and speed planning simultaneously; however, the existing methods only support simple-shaped corridors like cuboids, which restrict the search space for optimization in complex scenarios. We propose to use trapezoidal prism-shaped corridors for optimization, which significantly enlarges the solution space compared to the existing cuboidal corridors-based method. Finally, a piecewise B\'{e}zier curve optimization is conducted in our proposed corridors. This formulation theoretically guarantees the safety of the continuous-time trajectory. We validate the efficiency and effectiveness of the proposed approach in numerical and CommonRoad simulations.

翻译：安全保证的机动规划对于自行驾驶汽车产生无碰撞轨迹至关重要。为此目的,广泛采用了分层运动规划方法以及分解路径和速度规划。这种方法在存在动态障碍的情况下容易不优化。空间时空方法同时处理路径规划和速度规划;然而,现有方法只支持小熊等简单形状走廊,这些走廊限制了在复杂情况下寻求优化的空间。我们提议使用陷阱式棱镜形走廊优化,这大大扩大了溶液空间,而与现有的幼虫走廊法相比。最后,在我们拟议的走廊中进行了小块B\{e{e}zier曲线优化。这一提法在理论上保证了连续时间轨迹的安全。我们在数字和通用 Road模拟中验证了拟议方法的效率和效力。