Spatio-temporal Motion Planning for Autonomous Vehicles with Trapezoidal Prism Corridors and B\'{e}zier Curves

TL;DR

This paper introduces a novel spatio-temporal motion planning method for autonomous vehicles that uses trapezoidal prism corridors and Bézier curves, expanding the solution space and ensuring safety in dynamic environments.

Contribution

It proposes trapezoidal prism corridors for optimization, enlarging the search space over traditional cuboid corridors, and employs Bézier curve optimization to guarantee trajectory safety.

Findings

Enlarged solution space improves planning flexibility.

The method guarantees safety of continuous trajectories.

Validated effectiveness through simulations.

Abstract

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.