mlsanityData to 7 Oct 2026

A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing

arXiv 2209.15073 ↗First posted 29 Sep 2022cs.ROcited by 11

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

Abstract (arXiv)

Autonomous racing with scaled race cars has gained increasing attention as an effective approach for developing perception, planning and control algorithms for safe autonomous driving at the limits of the vehicle's handling. To train agile control policies for autonomous racing, learning-based approaches largely utilize reinforcement learning, albeit with mixed results. In this study, we benchmark a variety of imitation learning policies for racing vehicles that are applied directly or for bootstrapping reinforcement learning both in simulation and on scaled real-world environments. We show that interactive imitation learning techniques outperform traditional imitation learning methods and can greatly improve the performance of reinforcement learning policies by bootstrapping thanks to its better sample efficiency. Our benchmarks provide a foundation for future research on autonomous racing using Imitation Learning and Reinforcement Learning.

Key facts. Cited by 11 papers 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 12 of its 27 references that are in the map. First posted on arXiv 29 Sep 2022.

Builds on

12 of 27 references.

References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 302
  3. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
  4. 2018HG-DAgger: Interactive Imitation Learning with Human Expertscited by 245
  5. 2021ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learningcited by 98
  6. 2018CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-drivingcited by 96
  7. 2016Query-Efficient Imitation Learning for End-to-End Autonomous Drivingcited by 83
  8. 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 56
  9. 2021Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learningcited by 21
  10. 2020Learning from Simulation, Racing in Realitycited by 12
  11. 2022Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limitscited by 10
  12. 2022Teaching Autonomous Systems Hands-On: Leveraging Modular Small-Scale Hardware in the Robotics Classroomcited by 1

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 4; 2025: 4.

11 papers.

  1. 2023MEGA-DAgger: Imitation Learning with Multiple Imperfect Expertscited by 17
  2. 2023Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Predictioncited by 15
  3. 2024Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learningcited by 12
  4. 2025Dynamic Rank Adjustment in Diffusion Policies for Efficient and Flexible Trainingcited by 6
  5. 2025PRISM-DP: Spatial Pose-based Observations for Diffusion-Policies via Segmentation, Mesh Generation, and Pose Trackingcited by 5
  6. 2024TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model for F1TENTH Autonomous Racingcited by 4
  7. 2024Developing Path Planning with Behavioral Cloning and Proximal Policy Optimization for Path-Tracking and Static Obstacle Nudgingcited by 3
  8. 2024Optimizing Active Perception for Learning Simultaneous Viewpoint Selection and Manipulation with Diffusion Policycited by 3
  9. 2023Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learningcited by 1
  10. 2025Towards a Reward-Free Reinforcement Learning Framework for Vehicle Controlcited by 0
  11. 2025Mesh RAG: Retrieval Augmentation for Autoregressive Mesh Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture
  2. 2024A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
  3. 2024Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning
  4. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
  5. 2024TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
  6. 2019Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving
  7. 2020Learning to Drive (L2D) as a Low-Cost Benchmark for Real-World Reinforcement Learning
  8. 2021Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving