mlsanityData to 7 Oct 2026

Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars

arXiv 2107.09782 ↗First posted 20 Jul 2021cs.RO cs.GTcited by 1

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

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.

Key facts. Cited by 1 paper in the map (data through 7 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.

Builds on

4 of 16 references.

References · 4

  1. 2017Optimization-Based Autonomous Racing of 1:43 Scale RC Carscited by 47
  2. 2021Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Spacecited by 20
  3. 2017A Non-Cooperative Game Approach to Autonomous Racingcited by 18
  4. 2019Learning How to Autonomously Race a Car: a Predictive Control Approachcited by 14

Built on by

Cited by year

Citing papers per year: 2022: 1.

1 paper.

  1. 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 56

Similar, not cited

Close in wording, no citation either way

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  2. 2026Driving Context-guided Model Predictive Planning and Control for Autonomous Car Racing at the Limit and Beyond
  3. 2024Sampling-Based Motion Planning with Online Racing Line Generation for Autonomous Driving on Three-Dimensional Race Tracks
  4. 2023Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing
  5. 2026Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing
  6. 2021Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture
  7. 2023Integrating Expert Guidance for Efficient Learning of Safe Overtaking in Autonomous Driving Using Deep Reinforcement Learning
  8. 2025Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving