arXiv 2409.055872024-09-12cs.CV

DSDFormer：一种用于鲁棒高精度驾驶员分心识别的创新Transformer-Mamba框架

DSDFormer: An Innovative Transformer-Mamba Framework for Robust High-Precision Driver Distraction Identification

Sun Yat-Sen University（中山大学）；South China University of Technology（华南理工大学）；Shanghai Jiao Tong University（上海交通大学）；City University of Hong Kong（香港城市大学）

Junzhou Chen, Zirui Zhang, Jing Yu, Heqiang Huang, Ronghui Zhang, Xuemiao Xu, Bin Sheng, Hong Yan

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AI总结：

针对驾驶员分心识别中特征捕捉不足与标签噪声问题，提出融合Transformer与Mamba的DSDFormer框架及无监督TRCL标签修正方法，在多个数据集上取得SOTA性能且支持实时部署，提升了检测的准确性与鲁棒性。

摘要：

驾驶员分心仍是交通事故的主要诱因之一，对全球道路安全构成严重威胁。随着智能交通系统的发展，准确、实时地识别驾驶员分心状态已成为必要需求。然而，现有方法难以同时捕捉全局上下文特征与细粒度局部特征，同时还需应对训练数据集中的噪声标签问题。为解决这些挑战，我们提出DSDFormer——一种新型框架，通过双状态域注意力（Dual State Domain Attention，DSDA）机制融合Transformer与Mamba架构的优势，在长距离依赖建模与细节特征提取之间取得平衡，实现鲁棒的驾驶员行为识别。此外，我们提出时序推理置信学习（Temporal Reasoning Confident Learning，TRCL），这是一种无监督方法，利用视频序列中的时空相关性修正噪声标签。我们的模型在AUC-V1、AUC-V2和100-Driver数据集上取得了当前最优性能，并且在NVIDIA Jetson AGX Orin平台上展现出实时处理效率。大量实验结果证实，DSDFormer与TRCL显著提升了驾驶员分心检测的准确率与鲁棒性，为提升道路安全提供了可扩展的解决方案。

英文摘要：

Driver distraction remains a leading cause of traffic accidents, posing a critical threat to road safety globally. As intelligent transportation systems evolve, accurate and real-time identification of driver distraction has become essential. However, existing methods struggle to capture both global contextual and fine-grained local features while contending with noisy labels in training datasets. To address these challenges, we propose DSDFormer, a novel framework that integrates the strengths of Transformer and Mamba architectures through a Dual State Domain Attention (DSDA) mechanism, enabling a balance between long-range dependencies and detailed feature extraction for robust driver behavior recognition. Additionally, we introduce Temporal Reasoning Confident Learning (TRCL), an unsupervised approach that refines noisy labels by leveraging spatiotemporal correlations in video sequences. Our model achieves state-of-the-art performance on the AUC-V1, AUC-V2, and 100-Driver datasets and demonstrates real-time processing efficiency on the NVIDIA Jetson AGX Orin platform. Extensive experimental results confirm that DSDFormer and TRCL significantly improve both the accuracy and robustness of driver distraction detection, offering a scalable solution to enhance road safety.

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