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Conventional approaches rely on a single data modality (e.g., video, audio, or vehicle sensors), which limits their effectiveness in real-world conditions. Multimodal driver behavior recognition addresses these limitations by integrating complementary data sources to provide a holistic view of the driver\u2019s state. This paper introduces a dependable, real-time multimodal driver behavior recognition system that combines RGB video, acoustic signals, and geometric keypoints to improve transportation safety. The proposed framework employs lightweight transformer models, efficient video token thinning, noise-aware audio processing, and reinforcement learning-based dynamic path selection to balance accuracy and latency. 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