DL-IAPS and PJSO: A Path/Speed Decoupled Trajectory Optimization and its Application in Autonomous Driving
This addresses trajectory planning for autonomous vehicles, representing an incremental improvement over existing path/speed decoupled methods.
The paper tackles autonomous driving trajectory optimization by decoupling collision-free planning into path smoothing and speed optimization components, resulting in improved collision avoidance precision, control feasibility, and driving comfort compared to existing methods.
This paper presents a free space trajectory optimization algorithm of autonomous driving vehicle, which decouples the collision-free trajectory planning problem into a Dual-Loop Iterative Anchoring Path Smoothing (DL-IAPS) and a Piece-wise Jerk Speed Optimization (PJSO). The work leads to remarkable driving performance improvements including more precise collision avoidance, higher control feasibility and better driving comfort, as those are often hard to realize in other existing path/speed decoupled trajectory optimization methods. Our algorithm's efficiency, robustness and adaptiveness to complex driving scenarios have been validated by both simulations and real on-road tests.