Spline-Based Minimum-Curvature Trajectory Optimization for Autonomous Racing

TL;DR

This paper introduces a B-spline based trajectory optimization method for autonomous racing that reduces computational complexity and enhances smoothness, enabling faster online adaptation with limited dynamic data.

Contribution

It presents a novel minimum-curvature spline optimization approach that significantly reduces decision variables and computation time while maintaining optimality and smoothness guarantees.

Findings

Achieves similar optimality with 90% fewer decision variables.

Reduces computation time from seconds to milliseconds.

Provides mathematical smoothness guarantees and flexible control.

Abstract

We propose a novel B-spline trajectory optimization method for autonomous racing. We consider the unavailability of sophisticated race car and race track dynamics in early-stage autonomous motorsports development and derive methods that work with limited dynamics data and additional conservative constraints. We formulate a minimum-curvature optimization problem with only the spline control points as optimization variables. We then compare the current state-of-the-art method with our optimization result, which achieves a similar level of optimality with a 90% reduction on the decision variable dimension, and in addition offers mathematical smoothness guarantee and flexible manipulation options. We concurrently reduce the problem computation time from seconds to milliseconds for a long race track, enabling future online adaptation of the previously offline technique.