Multi-Frame Vision-Language Model for Long-form Reasoning in Driver Behavior Analysis
This work addresses safety for drivers and pedestrians by applying vision-language models to driver behavior analysis, though it appears incremental as it builds on existing multi-modal methods for a specific domain.
The paper tackles the problem of identifying risky driving behavior by creating a multi-modal instruction tuning dataset and a driver coaching inference system, enabling language models to learn visual instructions across various risky driving scenarios for dashcam-based coaching.
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.