# A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data

arXiv: https://arxiv.org/abs/2407.16680 · HTML: https://mlsanity.com/paper/2407.16680/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Temporal Difference Learning for Model Predictive Control](https://mlsanity.com/paper/2203.04955/) (2022)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [Model Predictive Path Integral Control using Covariance Variable Importance Sampling](https://mlsanity.com/paper/1509.01149/) (2015)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Optimization-Based Autonomous Racing of 1:43 Scale RC Cars](https://mlsanity.com/paper/1711.07300/) (2017)
- [Finetuning Offline World Models in the Real World](https://mlsanity.com/paper/2310.16029/) (2023)
- [A Survey on Simulators for Testing Self-Driving Cars](https://mlsanity.com/paper/2101.05337/) (2021)
- [Learn-to-Race: A Multimodal Control Environment for Autonomous Racing](https://mlsanity.com/paper/2103.11575/) (2021)
- [Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios](https://mlsanity.com/paper/2005.08664/) (2020)
- [TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge](https://mlsanity.com/paper/2205.15979/) (2022)
- [Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds](https://mlsanity.com/paper/2207.11136/) (2022)
- [A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential](https://mlsanity.com/paper/2312.14808/) (2023)
- [Acting upon Imagination: when to trust imagined trajectories in model based reinforcement learning](https://mlsanity.com/paper/2105.05716/) (2021)
- [Ignition: An End-to-End Supervised Model for Training Simulated Self-Driving Vehicles](https://mlsanity.com/paper/1806.11349/) (2018)

## Built on by (most cited first, 6 of 6)

- [A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7](https://mlsanity.com/paper/2504.09021/) (2025)
- [AARK: An Open Toolkit for Autonomous Racing Research](https://mlsanity.com/paper/2410.00358/) (2024)
- [Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning](https://mlsanity.com/paper/2412.03803/) (2024)
- [Dynamics Distillation for Efficient and Transferable Control Learning](https://mlsanity.com/paper/2605.01516/) (2026)
- [Deep Reinforcement-Learning-Guided Model Predictive Control for Preventing Overtakes in Autonomous Racing](https://mlsanity.com/paper/2607.20973/) (2026)
- [TRACK: Telemetry-Based Racing Analysis and Coaching Kit in Sim Racing Games](https://mlsanity.com/paper/2610.10061/) (2026)

## Abstract (arXiv)

Despite the availability of international prize-money competitions, scaled vehicles, and simulation environments, research on autonomous racing and the control of sports cars operating close to the limit of handling has been limited by the high costs of vehicle acquisition and management, as well as the limited physics accuracy of open-source simulators. In this paper, we propose a racing simulation platform based on the simulator Assetto Corsa to test, validate, and benchmark autonomous driving algorithms, including reinforcement learning (RL) and classical Model Predictive Control (MPC), in realistic and challenging scenarios. Our contributions include the development of this simulation platform, several state-of-the-art algorithms tailored to the racing environment, and a comprehensive dataset collected from human drivers. Additionally, we evaluate algorithms in the offline RL setting. All the necessary code (including environment and benchmarks), working examples, datasets, and videos are publicly released and can be found at: https://assetto-corsa-gym.github.io