MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perception

• URL: http://arxiv.org/abs/2504.02264v1
• Date: Thu, 03 Apr 2025 04:23:27 GMT
• Title: MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perception
• Authors: Wenzhuo Liu, Wenshuo Wang, Yicheng Qiao, Qiannan Guo, Jiayin Zhu, Pengfei Li, Zilong Chen, Huiming Yang, Zhiwei Li, Lening Wang, Tiao Tan, Huaping Liu,
• Abstract summary: MMTL-UniAD is a unified multi-modal multi-task learning framework.<n>It simultaneously recognizes driver behavior (e.g., looking around, talking), driver emotion (e.g., anxiety, happiness), vehicle behavior (e.g., parking, turning) and traffic context (e.g., traffic jam, traffic smooth)
• Score: 22.18509264125815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advanced driver assistance systems require a comprehensive understanding of the driver's mental/physical state and traffic context but existing works often neglect the potential benefits of joint learning between these tasks. This paper proposes MMTL-UniAD, a unified multi-modal multi-task learning framework that simultaneously recognizes driver behavior (e.g., looking around, talking), driver emotion (e.g., anxiety, happiness), vehicle behavior (e.g., parking, turning), and traffic context (e.g., traffic jam, traffic smooth). A key challenge is avoiding negative transfer between tasks, which can impair learning performance. To address this, we introduce two key components into the framework: one is the multi-axis region attention network to extract global context-sensitive features, and the other is the dual-branch multimodal embedding to learn multimodal embeddings from both task-shared and task-specific features. The former uses a multi-attention mechanism to extract task-relevant features, mitigating negative transfer caused by task-unrelated features. The latter employs a dual-branch structure to adaptively adjust task-shared and task-specific parameters, enhancing cross-task knowledge transfer while reducing task conflicts. We assess MMTL-UniAD on the AIDE dataset, using a series of ablation studies, and show that it outperforms state-of-the-art methods across all four tasks. The code is available on https://github.com/Wenzhuo-Liu/MMTL-UniAD.

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