Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing Supervision

• URL: http://arxiv.org/abs/2310.05202v4
• Date: Fri, 03 Jan 2025 08:56:43 GMT
• Title: Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing Supervision
• Authors: Cong Duan, Zixuan Liu, Jiahao Xia, Minghai Zhang, Jiacai Liao, Libo Cao,
• Abstract summary: Deep neural networks enable real-time monitoring of in-vehicle drivers, facilitating the timely prediction of distractions, fatigue, and potential hazards. Recent research has exposed unreliable cross-dataset driver behavior recognition due to a limited number of data samples and background noise. We propose a Score-Softmax classifier, which reduces the model overconfidence by enhancing category independence.
• Score: 6.891556476231427
• License:
• Abstract: Deep neural networks enable real-time monitoring of in-vehicle drivers, facilitating the timely prediction of distractions, fatigue, and potential hazards. This technology is now integral to intelligent transportation systems. Recent research has exposed unreliable cross-dataset driver behavior recognition due to a limited number of data samples and background noise. In this paper, we propose a Score-Softmax classifier, which reduces the model overconfidence by enhancing category independence. Imitating the human scoring process, we designed a two-dimensional dynamic supervisory matrix consisting of one-dimensional Gaussian-smoothed labels. The dynamic loss descent direction and Gaussian smoothing increase the uncertainty of training to prevent the model from falling into noise traps. Furthermore, we introduce a simple and convenient multi-channel information fusion method;it addresses the fusion issue among arbitrary Score-Softmax classification heads. We conducted cross-dataset experiments using the SFDDD, AUCDD, and the 100-Driver datasets, demonstrating that Score-Softmax improves cross-dataset performance without modifying the model architecture. The experiments indicate that the Score-Softmax classifier reduces the interference of background noise, enhancing the robustness of the model. It increases the cross-dataset accuracy by 21.34%, 11.89%, and 18.77% on the three datasets, respectively. The code is publicly available at https://github.com/congduan-HNU/SSoftmax.

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