Datasets:

OpenPAV
/

OpenPAV-trajectory

pretty_name: OpenPAV-Trajectory
task_categories:
  - tabular-regression
  - time-series-forecasting
  - other
tags:
  - autonomous-driving
  - transportation
  - trajectory
  - vehicle-dynamics
  - csv
  - tabular
  - trajectory-modeling
  - car-following-modeling
size_categories:
  - 1M<n<10M
configs:
  - config_name: Argoverse
    data_files:
      - split: train
        path: data/Argoverse/*.csv
  - config_name: CATS
    data_files:
      - split: train
        path: data/CATS/*.csv
  - config_name: MicroSimACC
    data_files:
      - split: train
        path: data/MicroSimACC/*.csv
  - config_name: Ohio
    data_files:
      - split: train
        path: data/Ohio/*.csv
  - config_name: OpenACC
    data_files:
      - split: train
        path: data/OpenACC/*.csv
  - config_name: Vanderbilt
    data_files:
      - split: train
        path: data/Vanderbilt/*.csv
viewer: true

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:

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

Contributors

Hang Zhou, Keke Long , Chengyuan Ma.