mlsanityData to 14 Oct 2026

Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning

arXiv 2412.03803 ↗First posted 5 Dec 2024cs.ROcited by 2

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

Abstract (arXiv)

Success in racing requires a unique combination of vehicle setup, understanding of the racetrack, and human expertise. Since building and testing many different vehicle configurations in the real world is prohibitively expensive, high-fidelity simulation is a critical part of racecar development. However, testing different vehicle configurations still requires expert human input in order to evaluate their performance on different racetracks. In this work, we present the first steps towards an autonomous test driver, trained using deep reinforcement learning, capable of evaluating changes in vehicle setup on racing performance while driving at the level of the best human drivers. In addition, the autonomous driver model can be tuned to exhibit more human-like behavioral patterns by incorporating imitation learning into the RL training process. This extension permits the possibility of driver-specific vehicle setup optimization.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 43 references that are in the map. First posted on arXiv 5 Dec 2024.

Builds on

12 of 43 references.

References · 12

  1. 2016Generative Adversarial Imitation Learningcited by 1,597
  2. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  3. 2020AWAC: Accelerating Online Reinforcement Learning with Offline Datasetscited by 671
  4. 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 319
  5. 2016Sample Efficient Actor-Critic with Experience Replaycited by 188
  6. 2018Kickstarting Deep Reinforcement Learningcited by 81
  7. 2018Reinforcement Learning from Imperfect Demonstrationscited by 75
  8. 2019Watch, Try, Learn: Meta-Learning from Demonstrations and Rewardcited by 28
  9. 2020DSAC: Distributional Soft Actor-Critic for Risk-Sensitive Reinforcement Learningcited by 8
  10. 2024A Simulation Benchmark for Autonomous Racing with Large-Scale Human Datacited by 6
  11. 2022Data augmentation for efficient learning from parametric expertscited by 5
  12. 2022An Adaptive Human Driver Model for Realistic Race Car Simulationscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Self driving algorithm for an active four wheel drive racecarcited by 0
  2. 2026Driving Context-guided Model Predictive Planning and Control for Autonomous Car Racing at the Limit and Beyondcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture
  2. 2021Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning
  3. 2025On learning racing policies with reinforcement learning
  4. 2024A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo
  5. 2023FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing
  6. 2022A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing
  7. 2020Deep Reinforcement Learning for Autonomous Driving: A Survey
  8. 2024Deep Reinforcement Learning for Local Path Following of an Autonomous Formula SAE Vehicle