SafeSmartDrive: Real-Time Traffic Environment Detection and Driver Behavior Monitoring With Machine and Deep Learning

Abstract

The advancement of intelligent transportation systems is crucial for improving road safety and optimizing traffic flow. In this paper, we present SafeSmartDrive, an integrated transportation monitoring system designed to detect and assess critical elements in the driving environment while simultaneously monitoring driver behavior. The system is structured into four key layers: perception, filtering and preparation, detection and classification, and alert. SafeSmartDrive focuses on two primary objectives: (1) detecting and assessing essential traffic elements, including vehicles (buses, cars, motorcycles, trucks, bicycles), traffic signs and lights, pedestrians, animals, infrastructure damage, accident classification, and traffic risk assessment, and (2) evaluating driver behavior across various road types, such as highways, secondary roads, and intersections. Machine learning and deep learning algorithms are employed throughout the system’s components. For traffic element detection, we utilize YOLOv9 in this paper, which outperforms previous versions like YOLOv7 and YOLOv8, achieving a precision of 83.1%. Finally, we present the evaluation of the SafeSmartDrive system’s real-time detection capabilities in a specific scenario in Casablanca. SafeSmartDrive’s comprehensive architecture offers a novel approach to improving road safety through the integration of advanced detection, classification, and risk assessment capabilities.

Keywords

• Road safety
• driver behavior
• real-time monitoring
• environment detection
• vehicle detection
• traffic signs