Modeling driver lane-changing aggressiveness under target-lane interference: A Bayesian approach using naturalistic...

Aggressive lane-changing behavior on highways can induce sharp lateral movements and significant speed variations, posing considerable traffic safety risks. Maximum lateral velocity effectively reflects both the vehicle's lateral motion and the intensity of the driver's lane-change maneuver, making it a key metric for analyzing lane-changing behavior. In this study, 1,646 lane-changing events were extracted from the real-world vehicle trajectory collected by CQSkyEyes, and their basic characteristics were analyzed using descriptive statistics. The Bayesian model was developed to examine how lane-change intensity varies under different environmental and driving conditions. The results indicate that under complex weather conditions, drivers tend to adopt more conservative lane-changing strategies, reflected in reduced maximum lateral velocity, especially when facing hazardous time-to-collision (TTC) levels. Moreover, lane change behavior is shaped by surrounding-vehicle interaction metrics; notably, the approach of a vehicle in the target lane increases the likelihood of aggressive maneuvers. These findings highlight maximum lateral velocity as a robust quantitative indicator of driver behavior, offering actionable implications for traffic safety management and autonomous driving system design.