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Multi-Frame Vision-Language Model for Long-form Reasoning in Driver Behavior Analysis

Hiroshi Takato, Hiroshi Tsutsui, Komei Soda, Hidetaka Kamigaito

TL;DR

The paper tackles the challenge of identifying and explaining risky driving behaviors by leveraging a multi-frame vision-language model (MF-VLM) that fuses road-facing and driver-facing dashcam footage with audio and instructional text. Built on a fine-tuned Video-LLaMA backbone, MF-VLM uses a dual-branch architecture (Vision-Language and Audio-Language) and a three-stage training regime to achieve long-form, evidence-based coaching explanations. The authors contribute an instruction-tuning dataset tailored to driving scenarios, demonstrate improved event recognition and open-question reasoning over a baseline, and show the practical impact for dashcam-based coaching in commercial fleets. This work has significance for scalable driver coaching, safety management, and transparent reasoning about causal driving events in real-world settings.

Abstract

Identifying risky driving behavior in real-world situations is essential for the safety of both drivers and pedestrians. However, integrating natural language models in this field remains relatively untapped. To address this, we created a novel multi-modal instruction tuning dataset and driver coaching inference system. Our primary use case is dashcam-based coaching for commercial drivers. The North American Dashcam Market is expected to register a CAGR of 15.4 percent from 2022 to 2027. Our dataset enables language models to learn visual instructions across various risky driving scenarios, emphasizing detailed reasoning crucial for effective driver coaching and managerial comprehension. Our model is trained on road-facing and driver-facing RGB camera footage, capturing the comprehensive scope of driving behavior in vehicles equipped with dashcams.

Multi-Frame Vision-Language Model for Long-form Reasoning in Driver Behavior Analysis

TL;DR

The paper tackles the challenge of identifying and explaining risky driving behaviors by leveraging a multi-frame vision-language model (MF-VLM) that fuses road-facing and driver-facing dashcam footage with audio and instructional text. Built on a fine-tuned Video-LLaMA backbone, MF-VLM uses a dual-branch architecture (Vision-Language and Audio-Language) and a three-stage training regime to achieve long-form, evidence-based coaching explanations. The authors contribute an instruction-tuning dataset tailored to driving scenarios, demonstrate improved event recognition and open-question reasoning over a baseline, and show the practical impact for dashcam-based coaching in commercial fleets. This work has significance for scalable driver coaching, safety management, and transparent reasoning about causal driving events in real-world settings.

Abstract

Identifying risky driving behavior in real-world situations is essential for the safety of both drivers and pedestrians. However, integrating natural language models in this field remains relatively untapped. To address this, we created a novel multi-modal instruction tuning dataset and driver coaching inference system. Our primary use case is dashcam-based coaching for commercial drivers. The North American Dashcam Market is expected to register a CAGR of 15.4 percent from 2022 to 2027. Our dataset enables language models to learn visual instructions across various risky driving scenarios, emphasizing detailed reasoning crucial for effective driver coaching and managerial comprehension. Our model is trained on road-facing and driver-facing RGB camera footage, capturing the comprehensive scope of driving behavior in vehicles equipped with dashcams.
Paper Structure (33 sections, 1 equation, 6 figures, 4 tables)

This paper contains 33 sections, 1 equation, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Task and Evaluation
  3. Task Settings
  4. Input
  5. Visual Information
  6. Audio Information
  7. Instruction
  8. Output
  9. Evaluation Metrics
  10. Event Recognition
  11. Open Question
  12. Dataset Construction
  13. Our Model: Multi-Frame Vision-Language Model
  14. Architecture
  15. Vision-Language Branch
  16. ...and 18 more sections

Figures (6)

  • Figure 1: Overview of our targeting coaching task.
  • Figure 2: Integration of road-facing and driver-facing RGB camera footage.
  • Figure 3: A frame of the inputted video to the models.
  • Figure 4: Coaching framework aligning vision-language model outputs with coaching instructions.
  • Figure 5: The example output from Video-LLaMA-2-13B-Finetuned.
  • ...and 1 more figures