Open-PAV (Open Production Automated Vehicle) is an open platform designed to facilitate data collection, scenario generation, and evaluation of production automated vehicle (PAV) behaviors. It integrates diverse datasets and calibrated vehicle models, making it an essential tool for researchers and developers aiming to study PAV dynamics and their impacts. The project encourages contributions from the research community and provides ready-to-use model parameters for seamless integration with simulation tools.
Currently the dataset has examined 13 open-source datasets from 7 providers, each providing distinct insights into AV behavior across various driving conditions and scenarios. They are:
- Argoverse 2 Motion Forecasting Dataset. Collected from Austin in Texas, Detroit in Michigan, Miami in Florida, Pittsburgh in Pennsylvania, Palo Alto in California, and Washington, D.C. by Argo AI with researchers from Carnegie Mellon University and the Georgia Institute of Technology. Available at - Argoverse 2 Motion Forcasting Dataset.
- CATS Open Datasets. Three datasets were gathered in Tampa, Florida, and Madison, Wisconsin by the CATS Lab. Available at - CATS Lab.
- Central Ohio ACC Datasets. Two datasets were collated in Ohio by UCLA Mobility Lab and Transportation Research Center. Available at - Advanced Driver Assistance System (ADAS)-Equipped Single-Vehicle Data for Central Ohio.
- MircoSimACC Dataset. Collected in four cities in Florida, including Delray Beach, Loxahatchee, Boca Raton, and Parkland by the Florida Atlantic University research group. Available at - microSIM-ACC.
- OpenACC Database. Four datasets were collected across Italy, Sweden, and Hungary by the European Commission's Joint Research Centre. Available at - data.europa.eu.
- Vanderbilt ACC Dataset. Collected in Nashville, Tennessee by Vanderbilt University research group. Available at - Adaptive Cruise Control Dataset.
- Waymo Open Dataset. Two datasets were collected in six cities including San Francisco, Mountain View, and Los Angeles in California, Phoenix in Arizona, Detroit in Michigan, and Seattle in Washington by Waymo. Available at - Waymo Motion Dataset and Vehicle trajectory data processed from the Waymo Open Dataset.
By organizing the data from the above datasets, we processed a unified trajectory dataset ULTra-AV, in which all data are represented using a standardized format. Available at - A unified longitudinal trajectory dataset for automated vehicle and ULTra-AV.
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Comprehensive Dataset:
- A comprehensive trajectory dataset has been compiled by seven research teams. This dataset encompasses trajectory data from 14 AV brands and 33 AV models, drawn from 13 open-source AV datasets. All data have been converted into a unified vectorized format to enable efficient access and analysis.
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Kinematic Model Calibration:
- Supports linear models, IDM models (for SUMO), Wiedemann-99 (for Vissim), and machine learning-based models.
- Includes pre-configured model parameters for direct use in traditional simulation software.
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Simulation Integration:
- Enables rapid and accurate simulation of automated vehicle behavior and analysis of their impacts.
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Community Collaboration:
- Designed to foster contributions and collaboration among researchers globally.
- Model Enhancements: Improved calibration modeling methodology.
- Simulation Integration: Configured packages for SUMO, Vissim, and basic parameters for models.
- Dataset Expansion: Added new open-source trajectory datasets from ULTra-AV datasets.
- Model Enhancements: Improved basic logic for the project.
Open-PAV consists of the following components:
- Data Process: Given the raw AV data (LiDAR, images, videos, trajectories) as input, this module converts data of the same type into a unified format and performs preliminary data cleaning.
- System Identification: Using the processed AV data, this module calibrates the vehicle kinematic models of automated vehicles and exports them for simulation use. Users can also directly input their AV software stack to test their developed AV system within this module.
- Scenario Generation: This module is designed to generate tailored safety-critical driving scenarios specific to a given AV model, aiming to evaluate its safety performance.
- Evaluation: The generated scenarios are fed into pre-configured simulators, such as SUMO, VISSIM, and CARLA, for more accurate safety evaluation.
Check the Open-PAV Documentation for more details.
- CATS Lab (PI: Xiaopeng Li)
- CAT Lab (PI: Zuduo Zheng)
- SHINE Lab (PI: Danjue Chen)
- NextGen-ITS Lab (PI: David Kan)
- Ke Ma**, Hang Zhou**, Keke Long** , Chengyuan Ma**.
We welcome contributions to Open-PAV! Our projects have three levels of contributors. They are:
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Raw Data Contributor (*): Contribute raw PAV data to the platform.
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Processed Data Contributor (**). Contribute processed PAV data based on our standard format to the platform.
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Evaluation Contributor (***). Contribute PAV software stacks or calibrated AV models by using “System Identification” module. Then run the “Evaluation” module in specific scenarios to evaluate its performance on, e.g., safety, mobility, energy, etc.
For other users, you can also report bugs and suggest improvements by submitting issues.
This project is partially supported by the Center for Connected and Automated Transportation (CCAT) through the project titled "Traffic Control based on CARMA Platform for Maximal Traffic Mobility and Safety", National Institute for Congestion Reduction (NICR) through "Transit Priority Phase II: Network Control in Realistic Settings with Heterogeneous Vehicles" and "Cordon-Metering Rules for Present-Day and Future Cities", and National Science Foundation (NSF) through "NSF CPS: Small: NSF-DST: Turning “Tragedy of the Commons (ToC)” into “Emergent Cooperative Behavior (ECB)” for Automated Vehicles at Intersections with Meta-Learning". We also sincerely appreciate all the dataset providers and contributors for making this work possible.
- Zhou, H., Ma, K., Liang, S., Li, X. and Qu, X., 2024. A unified longitudinal trajectory dataset for automated vehicle. Scientific Data, 11(1), p.1123.
- Ma, K., Zhou, H., Liang, Z. and Li, X., 2025. Automated vehicle microscopic energy consumption study (AV-Micro): Data collection and model development. Energy, 320, p.135096.
Open-PAV is released under the MIT License. See the LICENSE file for details.