Integrating visual large language model and reasoning chain for driver behavior analysis and risk assessment

Abstract

Driver behavior is a critical factor in driving safety, making the development of sophisticated distraction classification methods essential. Our study presents a Distracted Driving Classification (DDC) approach utilizing a visual Large Language Model (LLM), named the Distracted Driving Language Model (DDLM). The DDLM introduces whole-body human pose estimation to isolate and analyze key postural features-head, right hand, and left hand-for precise behavior classification and better interpretability. Recognizing the inherent limitations of LLMs, particularly their lack of logical reasoning abilities, we have integrated a reasoning chain framework within the DDLM, allowing it to generate clear, reasoned explanations for its assessments. Tailored specifically with relevant data, the DDLM demonstrates enhanced performance, providing detailed, context-aware evaluations of driver behaviors and corresponding risk levels. Notably outperforming standard models in both zero-shot and few-shot learning scenarios, as evidenced by tests on the 100-Driver dataset, the DDLM stands out as an advanced tool that promises significant contributions to driving safety by accurately detecting and analyzing driving distractions.

Keywords: Distracted driving classification; Driving risk assessment; Explainable AI; Large language model; Reasoning chain framework.