Render of Assetto CorsaFPV of Assetto CorsaOur advanced simulatorOur mid-range simulatorOverview. We propose a high-fidelity racing simulation platform based on Assetto Corsa that enables reproducible algorithm benchmarking, as well as data collection with human drivers.
AssettoCorsaGym. Racing simulation platform using Assetto Corsa for autonomous driving. Includes a human driver dataset, and a comparison of different algorithms.
Abstract
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, and datasets are publicly released and can be found here.
Videos
SAC from scratch vs SAC from demonstrations
SAC from Scratch vs. Using Human Demonstrations. Increased Jitter in training SAC from Scratch (left), while human demonstrations make the controls smoother (right).
Recovering Manuevers
Recovering maneuver. Delays are added artificially to force the car to lose control and recover it.
Human data collection
Supporting Open-Source Science
Tracks and cars. The four tracks and three cars used in our dataset. The tracks include Indianapolis (IND), an easy oval track; Barcelona (BRN), featuring 14 distinct corners; Austria (RBR), a balanced track with technical turns and high-speed straights; and Monza (MNZ), the most challenging track with high-speed sections and complex chicanes. The cars are the Mazda Miata NA (Miata) with a top speed of 197 km/h, the Dallara F317 (F317) with a top speed of 250 km/h, and the BMW Z4 GT3 (GT3) with a top speed of 280 km/h. This diverse array ensures a comprehensive dataset for evaluating driving algorithms.
We open-source a 64M-step dataset with 2.3M steps from human drivers and the remaining steps from Soft Actor-Critic (SAC) policies. Data was collected at UC San Diego and Graz University of Technology, involving 15 drivers completing at least five laps per track and car. Participants included a professional e-sports driver, four experts, five casual drivers, and five beginners. Data can be downloaded manually using the links or via this script.
| Car | Track | Human Data | SAC Data | Download | ||
|---|---|---|---|---|---|---|
| Stints | Laps | Steps | Steps | |||
| F317 | BRN | 70 | 247 | 612,557 | 10M | Download |
| F317 | MNZ | 19 | 117 | 288,582 | 10M | Download |
| F317 | RBR | 24 | 142 | 295,679 | 10M | Download |
| F317 | IND | 1 | 4 | 4,605 | 4M | Download |
| GT3 | BRN | 37 | 181 | 501,206 | 10M | Download |
| GT3 | RBR | 15 | 102 | 218,722 | 10M | Download |
| GT3 | MNZ | 13 | 85 | 221,123 | 10M | Download |
| Miata | BRN | 5 | 27 | 99,145 | 10M | Download |
| Miata | MNZ | 2 | 10 | 38,395 | - | Download |
| Miata | RBR | 3 | 12 | 32,971 | - | Download |
| Total | 189 | 927 | 2,312,985 | 64M | ||
A Simulation Platform for Autonomous Racing
Overview. Our proposed platform for autonomous racing. We provide interfaces (gray) that (1) connect a simulator (Assetto Corsa) to autonomous racing methods, and (2) allow for human data collection. Interfaces receive track information and state, and execute actions in the simulator. Datasets (purple) are collected using an ACTI (Assetto Corsa Telemetry Interface) tool.
Citation
If you find our work useful, please consider citing the paper as follows: