Real-Time Driver Behavior Detection for Alert Using Bootstrapped Cross-Validation and Optimized Resnet-50

Abstract

Driver behavior monitoring is essential for advancing driver assistance systems, particularly in detecting highrisk or distracted actions. This study introduces ResBoot-50, an enhanced ResNet-50-based model designed for driver behavior detection, trained and tested on a dataset State-Farm, MRLEye, and Drive&Act datasets to capture a diverse range of driving behaviors. To ensure robust evaluation, we incorporated bootstrap sampling techniques, which provided varied training and validation splits, enabling a more comprehensive assessment of model performance and generalizability. ResBoot-50 achieved exceptional performance, with a validation accuracy, precision, recall, and F1-score all approximately at 99.52%, underscoring its reliability across multiple behavior categories. The use of bootstrap testing has proved beneficial in reducing overfitting and enhancing model robustness, supporting the models readiness for real-world applications. These findings highlight the impact of bootstrap-based evaluation in driver behavior analysis and suggest significant potential for integrating ResBoot-50 into driver assistance systems to improve road safety.