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

TL;DR

DSDFormer combines Transformer and Mamba architectures with a novel attention mechanism and an unsupervised label refinement method to improve real-time, accurate driver distraction detection, addressing noisy labels and capturing both global and local features.

Contribution

The paper introduces DSDFormer, a new framework integrating Transformer and Mamba architectures with a dual attention mechanism, and TRCL, an unsupervised label refinement method, for robust driver distraction recognition.

Findings

Achieves state-of-the-art performance on multiple datasets.

Demonstrates real-time processing on NVIDIA Jetson AGX Orin.

Significantly improves accuracy and robustness in driver distraction detection.

Abstract

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.