Performance Analysis of AI-Based Impaired Driver Detection Systems
A Comprehensive Review of Sensor Fusion and Machine Learning Approaches Abstract —The integration of artificial intelligence (AI) and machine learning (ML) technologies in automotive driver monitoring systems has emerged as a critical solution for detecting impaired driving behaviors. This paper presents a comprehensive analysis of AI-based impaired driver detection systems, evaluating the performance characteristics of various sensor modalities, feature extraction methods, and classification algorithms. Through systematic review of recent developments in computer vision, physiological monitoring, and alcohol detection technologies, we examine the accuracy, reliability, and practical implementation challenges of these systems. Our analysis reveals that multi-modal sensor fusion approaches achieve superior performance compared to single-sensor systems, with combined vision-based and physiological monitoring achieving detection accuracies ranging from 85% to 96% across different ...