Using Drift Diffusion Model to Analyze Cars' Lane Change Decisions behind Heavy Vehicles

2025-09-12

AI Summary

Heavy vehicles (HVs) exacerbate rear blind zones and constrain following passenger cars’ speeds, triggering frequent lane changes that compromise traffic efficiency and safety. To address this, we introduce the drift-diffusion model (DDM)—a cognitive decision-making framework—into HV-following scenarios for the first time. Leveraging high-fidelity trajectory data from TGSIM v3, we employ trajectory clustering to detect lane-change intent and fit DDM parameters to quantify drivers’ evidence accumulation dynamics. Results show that smaller initial time headways, larger rearward distances in the target lane, wider gaps, and higher speed differentials all accelerate evidence accumulation and shorten decision times. This work advances the mechanistic understanding of how environmental factors dynamically modulate lane-change decisions at the cognitive level. It provides both theoretical foundations and a modeling paradigm for cooperative HV management and proactive lane-change warning systems in connected and automated environments.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Computer Vision: Diffusion Models for Vision

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Heavy vehicles (HVs) pose a significant challenge to maintaining a smooth traffic flow on the freeway because they are slower moving and create large blind spots. It is therefore desirable for the followers of HVs to perform lane changes (LCs) to achieve a higher speed and a safer driving environment. Understanding LC behaviors of vehicles behind HVs is important because LCs can lead to highway capacity drop and induce safety risks. In this paper, a drift-diffusion model (DDM) is proposed to model the LC behavior of cars behind HVs. In this drift-diffusion (DD) process, vehicles consider the surrounding traffic environment and accumulate evidence over time. A LC is made if the evidence threshold is exceeded. By obtaining vehicle trajectories with LC intentions in the Third Generation Simulation (TGSIM) dataset through clustering and fitting them with the DDM, we find that a lower initial headway makes the drivers more likely to LC. Furthermore, a larger distance to the follower on the target lane, an increasing target gap size, and a higher speed difference between the target lane and the leading HV increases the rate of evidence accumulation and leads to a LC execution sooner.

Problem

• Modeling lane change decisions of cars behind heavy vehicles
• Analyzing evidence accumulation process using drift diffusion model
• Identifying factors influencing lane change timing and execution

Innovation

• Drift-diffusion model for lane change decisions
• Clustering trajectories from TGSIM dataset
• Evidence accumulation based on traffic parameters