Online Velocity Profile Generation and Tracking for Sampling-Based Local Planning Algorithms in Autonomous Racing Environments

TL;DR

This paper introduces an online velocity planning method for autonomous racing that adapts to changing vehicle and environmental conditions, improving local trajectory tracking through a combined optimization and sampling approach.

Contribution

It proposes a novel online velocity profile generation technique that integrates a forward-backward solver with a 3D track sampling strategy for adaptive local planning.

Findings

Robust performance in racing scenarios

High computational efficiency

Sensitivity to deviations from racing line

Abstract

This work presents an online velocity planner for autonomous racing that adapts to changing dynamic constraints, such as grip variations from tire temperature changes and rubber accumulation. The method combines a forward-backward solver for online velocity optimization with a novel spatial sampling strategy for local trajectory planning, utilizing a three-dimensional track representation. The computed velocity profile serves as a reference for the local planner, ensuring adaptability to environmental and vehicle dynamics. We demonstrate the approach's robust performance and computational efficiency in racing scenarios and discuss its limitations, including sensitivity to deviations from the predefined racing line and high jerk characteristics of the velocity profile.