mlsanityData to 14 Oct 2026

Accelerating Real-World Overtaking in F1TENTH Racing Employing Reinforcement Learning Methods

arXiv 2510.26040 ↗First posted 30 Oct 2025cs.RO cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

While autonomous racing performance in Time-Trial scenarios has seen significant progress and development, autonomous wheel-to-wheel racing and overtaking are still severely limited. These limitations are particularly apparent in real-life driving scenarios where state-of-the-art algorithms struggle to safely or reliably complete overtaking manoeuvres. This is important, as reliable navigation around other vehicles is vital for safe autonomous wheel-to-wheel racing. The F1Tenth Competition provides a useful opportunity for developing wheel-to-wheel racing algorithms on a standardised physical platform. The competition format makes it possible to evaluate overtaking and wheel-to-wheel racing algorithms against the state-of-the-art. This research presents a novel racing and overtaking agent capable of learning to reliably navigate a track and overtake opponents in both simulation and reality. The agent was deployed on an F1Tenth vehicle and competed against opponents running varying competitive algorithms in the real world. The results demonstrate that the agent's training against opponents enables deliberate overtaking behaviours with an overtaking rate of 87% compared 56% for an agent trained just to race.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 25 references that are in the map. First posted on arXiv 30 Oct 2025.

Builds on

9 of 25 references.

References · 9

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  2. 2021Goal Misgeneralization in Deep Reinforcement Learningcited by 123
  3. 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 64
  4. 2021Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learningcited by 45
  5. 2019Stochastic Dynamic Games in Belief Spacecited by 23
  6. 2024ForzaETH Race Stack -- Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardwarecited by 8
  7. 2024TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model for F1TENTH Autonomous Racingcited by 6
  8. 2021Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racingcited by 5
  9. 2021Local NMPC on Global Optimised Path for Autonomous Racingcited by 2

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  1. 2024F1tenth Autonomous Racing With Offline Reinforcement Learning Methods
  2. 2024Unifying F1TENTH Autonomous Racing: Survey, Methods and Benchmarks
  3. 2021Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars
  4. 2026Autonomous overtaking trajectory optimization using reinforcement learning and opponent pose estimation
  5. 2024Trajectory Planning Using Reinforcement Learning for Interactive Overtaking Maneuvers in Autonomous Racing Scenarios
  6. 2024A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
  7. 2026Deep Reinforcement-Learning-Guided Model Predictive Control for Preventing Overtakes in Autonomous Racing
  8. 2021Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture