This paper presents a temporal action localization framework specifically designed for driver monitoring systems in autonomous driving applications. The framework employs a two-stage pipeline combining VideoMAE-based feature extraction with an Augmented Self-Mask Attention detector to identify hazardous driving behaviors from in-cabin video streams. A Spatial Pyramid Pooling-Fast module captures multi-scale temporal features for improved localization accuracy. The approach is optimized for transportation safety checkpoints and fleet management assessment systems, demonstrating a trade-off between model capacity and computational efficiency.