Real-Time Driver Behavior Recognition on Low-Cost Edge Hardware
Published:Dec 26, 2025 00:54
•1 min read
•ArXiv
Analysis
This paper addresses a critical need in automotive safety by developing a real-time driver monitoring system (DMS) that can run on inexpensive hardware. The focus on low latency, power efficiency, and cost-effectiveness makes the research highly practical for widespread deployment. The combination of a compact vision model, confounder-aware label design, and a temporal decision head is a well-thought-out approach to improve accuracy and reduce false positives. The validation across diverse datasets and real-world testing further strengthens the paper's contribution. The discussion on the potential of DMS for human-centered vehicle intelligence adds to the paper's significance.
Key Takeaways
- •Develops a real-time driver behavior recognition system for low-cost edge hardware.
- •Employs a compact vision model, confounder-aware label design, and temporal decision head for improved accuracy and reduced false positives.
- •Achieves real-time performance (16-25 FPS) on Raspberry Pi 5 and Google Coral Edge TPU.
- •Validates the system across diverse datasets and real-world in-vehicle tests.
- •Highlights the potential of DMS for human-centered vehicle intelligence.
Reference
“The system covers 17 behavior classes, including multiple phone-use modes, eating/drinking, smoking, reaching behind, gaze/attention shifts, passenger interaction, grooming, control-panel interaction, yawning, and eyes-closed sleep.”