# Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning

arXiv: https://arxiv.org/abs/2107.08325 · HTML: https://mlsanity.com/paper/2107.08325/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 18 Jul 2021.

## Builds on (most cited first, 22 of 22)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Model-Based Reinforcement Learning for Atari](https://mlsanity.com/paper/1903.00374/) (2019)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Deep Evidential Regression](https://mlsanity.com/paper/1910.02600/) (2019)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [Learning to Drive in a Day](https://mlsanity.com/paper/1807.00412/) (2018)
- [CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving](https://mlsanity.com/paper/1807.03776/) (2018)
- [Thinking Fast and Slow: Optimization Decomposition Across Timescales](https://mlsanity.com/paper/1704.07785/) (2017)
- [Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control](https://mlsanity.com/paper/1706.06491/) (2017)
- [Optimization-Based Autonomous Racing of 1:43 Scale RC Cars](https://mlsanity.com/paper/1711.07300/) (2017)
- [AMZ Driverless: The Full Autonomous Racing System](https://mlsanity.com/paper/1905.05150/) (2019)
- [High-speed Autonomous Drifting with Deep Reinforcement Learning](https://mlsanity.com/paper/2001.01377/) (2020)
- [Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusion](https://mlsanity.com/paper/2005.01935/) (2020)
- [VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments](https://mlsanity.com/paper/2004.12591/) (2020)
- [DeepRacing: Parameterized Trajectories for Autonomous Racing](https://mlsanity.com/paper/2005.05178/) (2020)
- [Vision-Based Autonomous Driving: A Model Learning Approach](https://mlsanity.com/paper/2003.08300/) (2020)

## Built on by (most cited first, 21 of 21)

- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [The Unreasonable Effectiveness of Deep Evidential Regression](https://mlsanity.com/paper/2205.10060/) (2022)
- [A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo](https://mlsanity.com/paper/2406.12563/) (2024)
- [DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks](https://mlsanity.com/paper/2108.05030/) (2021)
- [Safe Autonomous Racing via Approximate Reachability on Ego-vision](https://mlsanity.com/paper/2110.07699/) (2021)
- [A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing](https://mlsanity.com/paper/2209.15073/) (2022)
- [Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments](https://mlsanity.com/paper/2302.04823/) (2023)
- [Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning](https://mlsanity.com/paper/2109.08473/) (2021)
- [A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator](https://mlsanity.com/paper/2509.08221/) (2025)
- [Autonomous Driving Small-Scale Cars: A Survey of Recent Development](https://mlsanity.com/paper/2404.06229/) (2024)
- [Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing](https://mlsanity.com/paper/2205.02953/) (2022)
- [A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7](https://mlsanity.com/paper/2504.09021/) (2025)
- [DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments](https://mlsanity.com/paper/2207.09934/) (2022)
- [Developing Path Planning with Behavioral Cloning and Proximal Policy Optimization for Path-Tracking and Static Obstacle Nudging](https://mlsanity.com/paper/2409.05289/) (2024)
- [Robot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control](https://mlsanity.com/paper/2210.10639/) (2022)
- [SutureFormer: Learning Surgical Trajectories via Goal-conditioned Offline RL in Pixel Space](https://mlsanity.com/paper/2603.26720/) (2026)
- [MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigation](https://mlsanity.com/paper/2209.09079/) (2022)
- [Enhancing Robot Learning through Learned Human-Attention Feature Maps](https://mlsanity.com/paper/2308.15327/) (2023)
- [Generalizable Image Repair for Robust Visual Control](https://mlsanity.com/paper/2503.05911/) (2025)
- [Imitation Learning for Autonomous Driving: Insights from Real-World Testing](https://mlsanity.com/paper/2504.18847/) (2025)
- [Learning-based Autonomous Oversteer Control and Collision Avoidance](https://mlsanity.com/paper/2505.15275/) (2025)

## Abstract (arXiv)

Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computationally inefficient and sensitive to environmental changes. Recently, deep-learning-based end-to-end systems have shown promising results for autonomous driving/racing. However, they are commonly implemented by supervised imitation learning (IL), which suffers from the distribution mismatch problem, or by reinforcement learning (RL), which requires a huge amount of risky interaction data. In this work, we present a general deep imitative reinforcement learning approach (DIRL), which successfully achieves agile autonomous racing using visual inputs. The driving knowledge is acquired from both IL and model-based RL, where the agent can learn from human teachers as well as perform self-improvement by safely interacting with an offline world model. We validate our algorithm both in a high-fidelity driving simulation and on a real-world 1/20-scale RC-car with limited onboard computation. The evaluation results demonstrate that our method outperforms previous IL and RL methods in terms of sample efficiency and task performance. Demonstration videos are available at https://caipeide.github.io/autorace-dirl/