Datasets:
annotations_creators:
- machine-generated
language:
- en
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality: []
pretty_name: AssettoCorsaGym Dataset
size_categories:
- 10M<n<100M
source_datasets:
- original
tags:
- RL
- MBRL
- autonomous driving
- racing
- MPC
task_categories:
- other
task_ids: []
Dataset Card for Assetto Corsa Gym
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://dasgringuen.github.io/assetto_corsa_gym/
- Repository: https://github.com/dasGringuen/assetto_corsa_gym
- Paper:
- Leaderboard:
- Point of Contact: adrianremonda@gmail.com
Dataset Summary
The AssettoCorsaGym dataset comprises 64 million steps, including 2.3 million steps from human drivers and the remaining from Soft Actor-Critic (SAC) policies. Data collection involved 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.
Supported Tasks and Leaderboards
- Autonomous driving
- Reinforcement learning
- Behavior cloning
- Imitation learning
Languages
English
Dataset Structure
See https://github.com/dasGringuen/assetto_corsa_gym/blob/main/data/paths.yml and https://github.com/dasGringuen/assetto_corsa_gym/blob/main/data/README.md
<track>
<car>
<human / policy>
laps
Data Instances
Each data instance includes telemetry data at 50Hz from a racing simulator, such as speed, position, acceleration, and control inputs (steering, throttle, brake).
Data Fields
Data Splits
We split the data in cars and tracks
Dataset Creation
Curation Rationale
The Assetto Corsa Gym dataset was curated to advance research in autonomous driving, reinforcement learning, and imitation learning. By providing a diverse dataset that includes both human driving data and data generated by Soft Actor-Critic (SAC) policies
Source Data
Initial Data Collection and Normalization
Data was collected from a racing simulator set up. Human drivers completed at least five laps per track and car, while SAC policies were trained from scratch and their replay buffers were recorded.
Who are the source language producers?
Human drivers of varying skill levels, including a professional e-sports driver, experts, casual drivers, and beginners.
Annotations
Annotation process
Data was automatically labeled during collection to differentiate between human and SAC policy data.
Who are the annotators?
The data was annotated by the research team at UC San Diego and Graz University of Technology.
Personal and Sensitive Information
The dataset does not contain any personally identifiable information. Drivers were anonymized and identified only by driver_id.
Considerations for Using the Data
Social Impact of Dataset
The dataset aims to contribute to the development of safer and more efficient autonomous driving systems by providing diverse driving data for training machine learning models.
Discussion of Biases
The dataset includes a wide range of driving skills, but there may still be biases based on the limited number of human participants and their specific driving styles. Additionally, the number of laps per track and car is unbalanced, which might affect the generalizability of models trained on this dataset. The selection of tracks and cars, as well as the specific conditions under which the data was collected, could also introduce biases that researchers should be aware of when using this dataset.
Other Known Limitations
- Limited number of tracks and cars
- Simulated driving environment may not fully capture real-world driving conditions
Additional Information
Dataset Curators
The dataset was curated by researchers at UC San Diego and Graz University of Technology.
Licensing Information
CC BY 4.0
Contributions
Thanks to @dasGringuen for adding this dataset.