ArXiv

Controlled Experiments on Lane Changing by Transitional Autonomous Vehicle: Dataset and Behavioral Insights

2026-07-29 · 16:07 UTC ·Abhinav Sharma, Md Abdullah Al Hasan, Danjue Chen... ·1 min read
Authors
Abhinav Sharma, Md Abdullah Al Hasan, Danjue Chen...
Categories
cs.RO, eess.SY
arXiv
https://arxiv.org/abs/2607.27085v1
PDF
https://arxiv.org/pdf/2607.27085v1

Brief

The paper presents the NC-tALC dataset and a controlled field experiment (78 mandatory lane-change trials in Apex, NC) using four instrumented vehicles and RTK-GNSS/INS trajectories. It quantifies evolving lead–lag gaps and surrogate safety measures, finding gap convergence near lane crossing and peak collision risk at physical entry — typically driven by the target-lane leader. The dataset offers an empirical benchmark for AV lane-change modeling and safety validation and is one of the first repeatable public-road characterizations of complete mandatory lane-change behavior.

Why it matters

NC-tALC dataset: controlled public-road experiment of 78 mandatory lane-change trials in Apex, NC (published 2026-07-29) using four instrumented vehicles and high-resolution RTK-GNSS/INS trajectories to create repeatable traffic conditions and extract lead/lag/lane-change gaps.

Key details

  • Behavioral findings: despite varied initial gap positions, lead and lag gaps converged to a narrow range near lane crossing; potential collision risk rose through the maneuver, peaked near physical lane entry, was dominated by interactions with the target-lane leader, and often persisted after lane-change completion.
Source evidence

Abstract

This paper presents the North Carolina Transitional Autonomous Vehicle Lane-Changing (NC-tALC) dataset and uses it to characterize mandatory lane-changing behavior of transitional automated vehicles (tAVs). It quantifies the evolution of lead--lag gaps throughout the lane-change process and examines how potential collision risk develops during the maneuver. A controlled field experiment comprising 78 mandatory lane-change trials was conducted on a public roadway in Apex, North Carolina. Four instrumented vehicles created repeatable traffic conditions while varying the lane changer's initial position within the candidate target gap. High-resolution RTK-GNSS/INS trajectories were processed to identify key timestamps, calculate lead, lag, and lane-change gaps, and estimate interactions using time-gap- and speed-based surrogate safety measures. Despite substantial differences in initial conditions, lead and lag gaps consistently converged toward a relatively narrow range near lane crossing. Potential collision risk increased as the maneuver progressed, peaked near physical lane entry, and was dominated by interactions with the target-lane leader. Lane-change completion did not necessarily coincide with the disappearance of collision risk. This study provides one of the first controlled empirical characterizations of the complete mandatory lane-change process of tAVs using repeatable public-road experiments. The NC-tALC dataset supports analysis of behavioral and safety evolution throughout the maneuver rather than only at the gap-acceptance instant. The dataset and findings provide empirical benchmarks for evaluating automated lane-changing behavior, calibrating behavioral models, and validating simulation and safety assessment methods for mandatory lane-change scenarios.