A Unified Longitudinal Trajectory Dataset for Automated Vehicle

TL;DR

This paper introduces Ultra-AV, a comprehensive, cleaned, and validated dataset of longitudinal trajectories from 13 sources, enabling improved analysis and modeling of automated vehicle behaviors.

Contribution

The study creates a unified, high-quality longitudinal trajectory dataset for AVs, addressing data quality issues and providing standardized metrics for research and development.

Findings

Validated data through safety, mobility, stability, sustainability metrics

Analyzed relationships between variables in car-following models

Provided guidelines for data collection and model development

Abstract

Automated Vehicles (AVs) promise significant advances in transportation. Critical to these improvements is understanding AVs' longitudinal behavior, relying heavily on real-world trajectory data. Existing open-source trajectory datasets of AV, however, often fall short in refinement, reliability, and completeness, hindering effective performance metrics analysis and model development. This study addresses these challenges by creating a Unified Longitudinal TRAjectory dataset for AVs (Ultra-AV) to analyze their microscopic longitudinal driving behaviors. This dataset compiles data from 13 distinct sources, encompassing various AV types, test sites, and experiment scenarios. We established a three-step data processing: 1. extraction of longitudinal trajectory data, 2. general data cleaning, and 3. data-specific cleaning to obtain the longitudinal trajectory data and car-following trajectory data. The validity of the processed data is affirmed through performance evaluations across safety, mobility, stability, and sustainability, along with an analysis of the relationships between variables in car-following models. Our work not only furnishes researchers with standardized data and metrics for longitudinal AV behavior studies but also sets guidelines for data collection and model development.