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

Driver behavior is a critical factor in driving safety, making the development of sophisticated distraction classification methods essential. The 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, the authors 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.

• Record URL:
  https://doi.org/10.1016/j.aap.2024.107497
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0001457524000423
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhang, Kunpeng
  • 0000-0003-1979-4261
  • Wang, Shipu
  • Jia, Ning
  • Zhao, Liang
  • Han, Chunyang
  • Li, Li
  • 0000-0002-9428-1960
• Publication Date: 2024-4

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 107497
• Serial:
  • Accident Analysis & Prevention
  • Volume: 198
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Behavior; Distracted driving; Driver monitoring; Driver performance; Image analysis; Machine learning
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01909743
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 26 2024 8:51AM