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

Duan, C; Liu, Z; Xia, J; Zhang, M; Liao, J; Cao, L

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

Duan, C Liu, Z Xia, J

Zhang, M Liao, J Cao, L

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Intelligent Vehicles, 2024, PP, (99), pp. 1-14
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Duan, C
dc.contributor.author	Liu, Z
dc.contributor.author	Xia, J https://orcid.org/0000-0001-9628-9563
dc.contributor.author	Zhang, M
dc.contributor.author	Liao, J
dc.contributor.author	Cao, L
dc.date.accessioned	2024-09-05T09:38:40Z
dc.date.available	2024-09-05T09:38:40Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Intelligent Vehicles, 2024, PP, (99), pp. 1-14
dc.identifier.issn	2379-8858
dc.identifier.issn	2379-8858
dc.identifier.uri	http://hdl.handle.net/10453/180661
dc.description.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 <uri>https://github.com/congduan-HNU/SSoftmax.</uri>
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Intelligent Vehicles
dc.relation.isbasedon	10.1109/TIV.2024.3412198
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	4002 Automotive engineering
dc.subject.classification	4007 Control engineering, mechatronics and robotics
dc.subject.classification	4603 Computer vision and multimedia computation
dc.title	Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier and Dynamic Gaussian Smoothing Supervision
dc.type	Journal Article
utslib.citation.volume	PP
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-09-05T09:38:28Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

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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http://hdl.handle.net/10453/180661