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

2026-07-29

AI Summary

This study addresses the empirical gap in understanding dynamic interactions and risk evolution during mandatory lane changes of transitional-level automated vehicles. Through controlled on-road experiments, the authors collected 78 high-precision trajectory records (NC-tALC dataset), capturing the complete lane-change process for the first time and overcoming the conventional limitation of focusing solely on gap acceptance moments. Leveraging RTK-GNSS/INS positioning, standardized scenario design, and multidimensional surrogate safety measures, the study reveals key patterns: regardless of initial conditions, the longitudinal distances to surrounding vehicles converge to a narrow range near the lane-crossing point; collision risk peaks upon entering the target lane, primarily driven by the lead vehicle in that lane; and risk does not necessarily dissipate upon completion of the lane change.

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.

Problem

• transitional autonomous vehicle
• mandatory lane-changing
• collision risk
• behavioral characterization
• controlled experiment

Innovation

• transitional autonomous vehicle
• mandatory lane changing
• controlled field experiment
• surrogate safety measures
• RTK-GNSS/INS trajectories