How vehicles change lanes after encountering crashes: Empirical analysis and modeling

2026-01-13

AI Summary

This study addresses the high-risk scenarios arising from ambiguous post-accident lane-changing behaviors and non-yielding vehicles in target lanes. To this end, it presents the first systematic characterization of the unique dynamics of post-accident lane changes, constructs a novel lane-changing dataset derived from drone-captured video, and proposes an interaction-aware trajectory prediction framework. The framework incorporates a graph attention module explicitly modeling non-yielding behavior as an auxiliary task, integrating graph attention mechanisms, conditional variational autoencoders, and a Transformer decoder. Experimental results demonstrate that the model outperforms baseline methods by over 10% in both average displacement error and final displacement error across multiple prediction horizons, significantly improving conflict prediction accuracy while reducing false alarm rates. Furthermore, the model exhibits strong transferability across diverse geographic locations.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

When a traffic crash occurs, following vehicles need to change lanes to bypass the obstruction. We define these maneuvers as post crash lane changes. In such scenarios, vehicles in the target lane may refuse to yield even after the lane change has already begun, increasing the complexity and crash risk of post crash LCs. However, the behavioral characteristics and motion patterns of post crash LCs remain unknown. To address this gap, we construct a post crash LC dataset by extracting vehicle trajectories from drone videos captured after crashes. Our empirical analysis reveals that, compared to mandatory LCs (MLCs) and discretionary LCs (DLCs), post crash LCs exhibit longer durations, lower insertion speeds, and higher crash risks. Notably, 79.4% of post crash LCs involve at least one instance of non yielding behavior from the new follower, compared to 21.7% for DLCs and 28.6% for MLCs. Building on these findings, we develop a novel trajectory prediction framework for post crash LCs. At its core is a graph based attention module that explicitly models yielding behavior as an auxiliary interaction aware task. This module is designed to guide both a conditional variational autoencoder and a Transformer based decoder to predict the lane changer's trajectory. By incorporating the interaction aware module, our model outperforms existing baselines in trajectory prediction performance by more than 10% in both average displacement error and final displacement error across different prediction horizons. Moreover, our model provides more reliable crash risk analysis by reducing false crash rates and improving conflict prediction accuracy. Finally, we validate the model's transferability using additional post crash LC datasets collected from different sites.

Problem

• post crash lane change
• non-yielding behavior
• trajectory prediction
• crash risk
• lane change modeling

Innovation

• post-crash lane change
• graph-based attention
• interaction-aware modeling
• trajectory prediction
• yielding behavior