TEM^3-Learning: Time-Efficient Multimodal Multi-Task Learning for Advanced Assistive Driving

• URL: http://arxiv.org/abs/2506.18084v1
• Date: Sun, 22 Jun 2025 16:12:27 GMT
• Title: TEM^3-Learning: Time-Efficient Multimodal Multi-Task Learning for Advanced Assistive Driving
• Authors: Wenzhuo Liu, Yicheng Qiao, Zhen Wang, Qiannan Guo, Zilong Chen, Meihua Zhou, Xinran Li, Letian Wang, Zhiwei Li, Huaping Liu, Wenshuo Wang,
• Abstract summary: TEM3-Learning is a novel framework that jointly optimize driver emotion recognition, driver behavior recognition, traffic context recognition, and vehicle behavior recognition.<n>It achieves state-of-the-art accuracy across all four tasks, maintaining a lightweight architecture with fewer than 6 million parameters and delivering an impressive 142.32 FPS inference speed.
• Score: 22.22943635900334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-task learning (MTL) can advance assistive driving by exploring inter-task correlations through shared representations. However, existing methods face two critical limitations: single-modality constraints limiting comprehensive scene understanding and inefficient architectures impeding real-time deployment. This paper proposes TEM^3-Learning (Time-Efficient Multimodal Multi-task Learning), a novel framework that jointly optimizes driver emotion recognition, driver behavior recognition, traffic context recognition, and vehicle behavior recognition through a two-stage architecture. The first component, the mamba-based multi-view temporal-spatial feature extraction subnetwork (MTS-Mamba), introduces a forward-backward temporal scanning mechanism and global-local spatial attention to efficiently extract low-cost temporal-spatial features from multi-view sequential images. The second component, the MTL-based gated multimodal feature integrator (MGMI), employs task-specific multi-gating modules to adaptively highlight the most relevant modality features for each task, effectively alleviating the negative transfer problem in MTL. Evaluation on the AIDE dataset, our proposed model achieves state-of-the-art accuracy across all four tasks, maintaining a lightweight architecture with fewer than 6 million parameters and delivering an impressive 142.32 FPS inference speed. Rigorous ablation studies further validate the effectiveness of the proposed framework and the independent contributions of each module. The code is available on https://github.com/Wenzhuo-Liu/TEM3-Learning.

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