A Driving-Primitive-Based Framework for Modeling the Evolution of Unprotected Left-Turn Interactions Using UAV Trajectory Data

Unprotected left turns at urban intersections require drivers to continually regulate their driving behavior while negotiating conflicts with opposing through traffic. Existing studies have mostly examined such maneuvers through gap-acceptance decisions and surrogate conflict indicators, which cannot reflect how driving behavior changes for unprotected left-turn interaction events. To explore the evolution of unprotected left-turn interactions, this study develops a data-driven framework to decompose continuous left-turn trajectories into interpretable, variable-length driving primitives. Using high-resolution unmanned aerial vehicle trajectory data, 2490 valid left-turning and opposing-through vehicle interaction events were extracted. A Non-Homogeneous Hidden Markov Model was adopted to segment each interaction event into driving primitives. These primitives are then clustered using Time-Series K-Means with Dynamic Time Warping. The clustering results yielded six behavior patterns: cautious turning, low-speed waiting, accelerating departure, deceleration observation, intensive turning, and steady driving. These patterns were then mapped back to the temporal sequence of each interaction event to construct behavioral transition chains. The results demonstrate that an unprotected left turn typically evolves as an ordered combination of the identified behavior patterns, from waiting and observation to turning and departure, and that transitions among these patterns are associated with changing opposing-traffic conditions.