Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events...

Area of Science:

• Engineering
• Computer Science
• Human Factors

Background:

• Human factors significantly contribute to vehicle accidents.
• Driver monitoring systems (DMS) are crucial for enhancing driving safety by mitigating driver error.
• Artificial Intelligence (AI) offers advanced capabilities for improving DMS efficiency and accuracy.

Purpose of the Study:

• To consolidate and review state-of-the-art AI tools for driver monitoring and safety-critical event (SCE) analysis.
• To provide an inaugural overview of AI applications in driver monitoring from both academic and industry viewpoints.
• To identify key AI tools and models used in gaze analysis, driver state monitoring, and SCE analysis.

Main Methods:

• Extensive literature review of AI models and sensors in driver monitoring.
• Analysis of AI tools used in camera-based driver monitoring and SCE analysis.
• Comparative study of AI tool utilization in academic research versus industry applications.

Main Results:

• Identification of prevalent AI models and sensors for driver gaze and state monitoring.
• Cataloging of essential AI tools employed in industry for driver monitoring and SCE analysis.
• Highlighting discrepancies between academic research trends and industry adoption of AI tools.

Conclusions:

• AI tools are pivotal in advancing driver monitoring systems for enhanced safety.
• Understanding the landscape of AI tools is crucial for future research and development in automotive safety.
• Bridging the gap between academic insights and industry implementation can accelerate the adoption of effective AI-driven safety solutions.