OpenPAV-Trajectory

Dataset Description

OpenPAV-Trajectory is a curated collection of longitudinal vehicle-following trajectories for production automated vehicles (PAVs). It is part of the OpenPAV platform, which supports data collection, behavior modeling, and performance evaluation for production automated driving systems.

This release standardizes public trajectory datasets into one common tabular schema centered on two vehicles: a lead vehicle (LV) and a following automated vehicle (FAV).

The dataset is intended for car-following analysis, trajectory modeling, calibration of behavioral models, benchmarking, and simulation-oriented automated driving research.

OpenPAV project page: https://openpav.github.io/OpenPAV

Key Facts

• 12 CSV files from 6 data providers
• approximately 3,537,455 rows in total
• approximately 675 MB of raw CSV data
• unified schema across all files
• stored as provider-specific subsets for straightforward loading on the Hugging Face Hub

Repository Structure

OpenPAV-Trajectory/
├── README.md
├── Dataset.png
└── data/
    ├── Argoverse/
    ├── CATS/
    ├── MicroSimACC/
    ├── Ohio/
    ├── OpenACC/
    └── Vanderbilt/

Each provider directory is exposed as a separate Hugging Face dataset configuration:

• Argoverse
• CATS
• MicroSimACC
• Ohio
• OpenACC
• Vanderbilt

Load the Dataset

from datasets import load_dataset

dataset = load_dataset("YOUR_USERNAME/OpenPAV-Trajectory", "OpenACC")
print(dataset["train"])

To load a specific CSV manually:

from datasets import load_dataset

dataset = load_dataset(
    "csv",
    data_files="data/OpenACC/step3_ZalaZone.csv",
)

Data Schema

All CSV files follow the same schema.

Column	Description	Unit
Trajectory_ID	Unique identifier of a longitudinal trajectory	N/A
Time_Index	Timestamp within a trajectory	s
ID_LV	Lead vehicle ID	N/A
Type_LV	Lead vehicle type: automated vehicle = 1, human-driven vehicle = 0	N/A
Pos_LV	Lead vehicle position in Frenet coordinates	m
Speed_LV	Lead vehicle speed	m/s
Acc_LV	Lead vehicle acceleration	m/s^2
ID_FAV	Following automated vehicle ID	N/A
Pos_FAV	Following automated vehicle position in Frenet coordinates	m
Speed_FAV	Following automated vehicle speed	m/s
Acc_FAV	Following automated vehicle acceleration	m/s^2
Spatial_Gap	Bumper-to-bumper spacing between LV and FAV	m
Spatial_Headway	Center-to-center distance between LV and FAV	m
Speed_Diff	Relative speed defined as Speed_LV - Speed_FAV	m/s

Source Datasets

This integrated release currently standardizes public data from the following sources:

• Argoverse 2 Motion Forecasting Dataset
• CATS Open Datasets
• Central Ohio ACC Datasets
• MicroSimACC Dataset
• OpenACC Database
• Vanderbilt ACC Dataset

These sources cover multiple cities, road environments, and automated driving scenarios. The current repository contains transformed and harmonized trajectory tables derived from those public resources.

Contributing Data

We welcome contributions of PAV trajectory datasets.

Please follow these steps:

1. Fork this dataset repository.
2. Upload your dataset following the structure described below.
3. Submit a Pull Request.
4. The maintainers will review and merge the dataset.

Contributors

• Hang Zhou, Keke Long , Chengyuan Ma.