  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.
