dasgringuen/assettoCorsaGym

Dataset Card for Assetto Corsa Gym

Dataset Summary

The Assetto Corsa Gym 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

See: https://github.com/dasGringuen/assetto_corsa_gym/blob/main/assetto_corsa_gym/assetto-corsa-autonomous-racing-plugin/plugins/sensors_par/structures.py

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

Citation Information

@misc{remonda2024simulation,
  title={A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data}, 
  author={Adrian Remonda and Nicklas Hansen and Ayoub Raji and Nicola Musiu and Marko Bertogna and Eduardo E. Veas and Xiaolong Wang},
  booktitle={38th Annual Conference on Neural Information Processing Systems (Submission)},
  year={2024}
}

Contributions

Thanks to @dasGringuen for adding this dataset.