# Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars

arXiv: https://arxiv.org/abs/2107.09782 · HTML: https://mlsanity.com/paper/2107.09782/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Placed in

- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Builds on (most cited first, 4 of 4)

- [Optimization-Based Autonomous Racing of 1:43 Scale RC Cars](https://mlsanity.com/paper/1711.07300/) (2017)
- [Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space](https://mlsanity.com/paper/2102.09812/) (2021)
- [A Non-Cooperative Game Approach to Autonomous Racing](https://mlsanity.com/paper/1712.03913/) (2017)
- [Learning How to Autonomously Race a Car: a Predictive Control Approach](https://mlsanity.com/paper/1901.08184/) (2019)

## Built on by (most cited first, 1 of 1)

- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)

## Abstract (arXiv)

The rising popularity of driver-less cars has led to the research and development in the field of autonomous racing, and overtaking in autonomous racing is a challenging task. Vehicles have to detect and operate at the limits of dynamic handling and decisions in the car have to be made at high speeds and high acceleration. One of the most crucial parts in autonomous racing is path planning and decision making for an overtaking maneuver with a dynamic opponent vehicle. In this paper we present the evaluation of a track based offline policy learning approach for autonomous racing. We define specific track portions and conduct offline experiments to evaluate the probability of an overtaking maneuver based on speed and position of the ego vehicle. Based on these experiments we can define overtaking probability distributions for each of the track portions. Further, we propose a switching MPCC controller setup for incorporating the learnt policies to achieve a higher rate of overtaking maneuvers. By exhaustive simulations, we show that our proposed algorithm is able to increase the number of overtakes at different track portions.