mlsanityData to 14 Oct 2026

Deep Reinforcement Learning for Local Path Following of an Autonomous Formula SAE Vehicle

arXiv 2401.02903 ↗First posted 5 Jan 2024cs.RO cs.LGcited by 1

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

also in 1 more
Abstract (arXiv)

With the continued introduction of driverless events to Formula:Society of Automotive Engineers (F:SAE) competitions around the world, teams are investigating all aspects of the autonomous vehicle stack. This paper presents the use of Deep Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) to map locally-observed cone positions to a desired steering angle for race track following. Two state-of-the-art algorithms not previously tested in this context: soft actor critic (SAC) and adversarial inverse reinforcement learning (AIRL), are used to train models in a representative simulation. Three novel reward functions for use by RL algorithms in an autonomous racing context are also discussed. Tests performed in simulation and the real world suggest that both algorithms can successfully train models for local path following. Suggestions for future work are presented to allow these models to scale to a full F:SAE vehicle.

Key facts. Cited by 1 paper 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 12 of its 23 references that are in the map. First posted on arXiv 5 Jan 2024.

Builds on

12 of 23 references.

Closest ancestors · 1

Same direction, newest first

  1. 2020Deep Reinforcement Learning for Autonomous Driving: A Survey2 Feb

Other references · 11

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  4. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 490
  5. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  6. 2020Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehiclescited by 110
  7. 2021Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learningcited by 45
  8. 2018End-to-End Race Driving with Deep Reinforcement Learningcited by 40
  9. 2019AMZ Driverless: The Full Autonomous Racing Systemcited by 29
  10. 2019Explorations and Lessons Learned in Building an Autonomous Formula SAE Car from Simulationscited by 6
  11. 2023Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Carcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Path-conditioned Reinforcement Learning-based Local Planning for Long-Range Navigationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning
  2. 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
  3. 2024Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning
  4. 2019Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning
  5. 2022Driving in Real Life with Inverse Reinforcement Learning
  6. 2024Self-Driving Car Racing: Application of Deep Reinforcement Learning
  7. 2023Review of Deep Reinforcement Learning for Autonomous Driving
  8. 2021Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture