Introducing the transitional autonomous vehicle lane-changing dataset: Empirical Experiments

TL;DR

This paper introduces the NC-tALC dataset, a high-resolution trajectory dataset capturing transitional autonomous vehicle lane-changing behaviors in controlled experiments, to facilitate analysis of tAV interactions and safety during lane changes.

Contribution

The study presents a novel, high-fidelity dataset specifically designed for analyzing tAV lane-changing interactions, filling a critical gap in available empirical data for this emerging domain.

Findings

Provides 152 high-precision trajectory trials of tAV lane-changing maneuvers.

Enables analysis of tAV decision-making and response dynamics during lane changes.

Facilitates future research on traffic safety and stability involving tAVs.

Abstract

Transitional autonomous vehicles (tAVs), which operate beyond SAE Level 1-2 automation but short of full autonomy, are increasingly sharing the road with human-driven vehicles (HDVs). As these systems interact during complex maneuvers such as lane changes, new patterns may emerge with implications for traffic stability and safety. Assessing these dynamics, particularly during mandatory lane changes, requires high-resolution trajectory data, yet datasets capturing tAV lane-changing behavior are scarce. This study introduces the North Carolina Transitional Autonomous Vehicle Lane-Changing (NC-tALC) Dataset, a high-fidelity trajectory dataset designed to characterize tAV interactions during lane-changing maneuvers. The dataset includes two controlled experimental series. In the first, tAV lane-changing experiments, a tAV executes lane changes in the presence of adaptive cruise control (ACC) equipped target vehicles, enabling analysis of lane-changing execution. In the second, tAV responding experiments, two tAVs act as followers and respond to cut-in maneuvers initiated by another tAV, enabling analysis of follower response dynamics. The dataset contains 152 trials (72 lane-changing and 80 responding trials) sampled at 20 Hz with centimeter-level RTK-GPS accuracy. The NC-tALC dataset provides a rigorous empirical foundation for evaluating tAV decision-making and interaction dynamics in controlled mandatory lane-changing scenarios.