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arXiv · 2502.03005

Driver Assistance System Based on Multimodal Data Hazard Detection

Long Zhouxiang·Ovanes Petrosian
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Abstract

Autonomous driving technology has advanced significantly, yet detecting driving anomalies remains a major challenge due to the long-tailed distribution of driving events. Existing methods primarily rely on single-modal road condition video data, which limits their ability to capture rare and unpredictable driving incidents. This paper proposes a multimodal driver assistance detection system that integrates road condition video, driver facial video, and audio data to enhance incident recognition accuracy. Our model employs an attention-based intermediate fusion strategy, enabling end-to-end learning without separate feature extraction. To support this approach, we develop a new three-modality dataset using a driving simulator. Experimental results demonstrate that our method effectively captures cross-modal correlations, reducing misjudgments and improving driving safety.

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Long Zhouxiang, Ovanes Petrosian. 2025-02-05. Driver Assistance System Based on Multimodal Data Hazard Detection. https://arxiv.org/abs/2502.03005

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