# Adaptive Lookahead Pure-Pursuit for Autonomous Racing

arXiv: https://arxiv.org/abs/2111.08873 · HTML: https://mlsanity.com/paper/2111.08873/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2021.

## Built on by (most cited first, 4 of 4)

- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [A Sim-to-Real Vision-based Lane Keeping System for a 1:10-scale Autonomous Vehicle](https://mlsanity.com/paper/2409.18097/) (2024)
- [Dynamic Lookahead Distance via Reinforcement Learning-Based Pure Pursuit for Autonomous Racing](https://mlsanity.com/paper/2603.28625/) (2026)
- [Correcting Learning-based Perception for Safety](https://mlsanity.com/paper/2609.22108/) (2026)

## Abstract (arXiv)

This paper presents an adaptive lookahead pure-pursuit lateral controller for optimizing racing metrics such as lap time, average lap speed, and deviation from a reference trajectory in an autonomous racing scenario. We propose a greedy algorithm to compute and assign optimal lookahead distances for the pure-pursuit controller for each waypoint on a reference trajectory for improving the race metrics. We use a ROS based autonomous racing simulator to evaluate the adaptive pure-pursuit algorithm and compare our method with several other pure-pursuit based lateral controllers. We also demonstrate our approach on a scaled real testbed using a F1/10 autonomous racecar. Our method results in a significant improvement (20%) in the racing metrics for an autonomous racecar.