Research on Driving Behavior Risk Identification and Safety Assessment Methods Based on Artificial Intelligence

Abstract

Road traffic safety remains a critical challenge in modern transportation systems, with human error contributing to approximately 94% of serious traffic crashes. This research develops a comprehensive artificial intelligence framework for driving behavior risk identification and safety assessment through multi-dimensional data analysis and machine learning algorithms. The proposed methodology integrates heterogeneous data sources including vehicle kinematics, environmental conditions, and driver physiological signals to construct a probabilistic risk assessment model. Our approach employs deep neural networks for feature extraction and temporal pattern recognition, achieving 92.3% accuracy in high-risk behavior detection across diverse driving scenarios. The framework incorporates a novel risk quantification index that combines behavioral patterns with contextual factors, enabling real-time safety assessment. Experimental validation demonstrates 15.7% improvement in risk prediction accuracy compared to existing methods while maintaining computational efficiency suitable for embedded vehicular systems. The developed safety assessment indices provide interpretable risk scores that facilitate proactive intervention strategies in intelligent transportation systems.