Dual-sensing driving detection model

TL;DR

This paper introduces a dual-sensing driver fatigue detection system that combines computer vision and physiological signals, achieving higher accuracy and reliability than traditional single-modality methods for safer driving.

Contribution

It presents a novel architecture integrating real-time facial analysis with physiological signals and advanced fusion strategies, enhancing fatigue detection performance.

Findings

Outperforms traditional methods in accuracy and reliability.

Effective in controlled and real-world driving scenarios.

Demonstrates potential to reduce fatigue-related accidents.

Abstract

In this paper, a novel dual-sensing driver fatigue detection method combining computer vision and physiological signal analysis is proposed. The system exploits the complementary advantages of the two sensing modalities and breaks through the limitations of existing single-modality methods. We introduce an innovative architecture that combines real-time facial feature analysis with physiological signal processing, combined with advanced fusion strategies, for robust fatigue detection. The system is designed to run efficiently on existing hardware while maintaining high accuracy and reliability. Through comprehensive experiments, we demonstrate that our method outperforms traditional methods in both controlled environments and real-world conditions, while maintaining high accuracy. The practical applicability of the system has been verified through extensive tests in various driving scenarios and shows great potential in reducing fatigue-related accidents. This study contributes to the field by providing a more reliable, cost-effective, and humane solution for driver fatigue detection.