In the context of vehicle operation, detecting and identifying abnormal driver behaviors is challenging due to the complex in-car environment, changing lighting, and varied driver postures. Our approach, utilizing supervised contrastive learning, categorizes driver behaviors as normal or abnormal. We employ depth images from the driver's front and above to address environmental complexities and improve accuracy. Our enhanced 3D-MobileNetV2architecture achieves impressive results on the DAD dataset test set, with a 94.18% accuracy rate and a 0.962AUC, validating the effectiveness of our method in driver anomaly detection.
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