Learning Driver-Irrelevant Features for Generalizable Driver Behavior Recognition
Traffic accidents caused by driver distractions have seriously endangered public safety, with driver distractions typically stemming from behaviors beyond safe driving. Recently, vision-based driver behavior recognition has attracted much attention, achieving great success with deep learning-based schemes. However, the generalization ability of these models in real-world scenarios remains unsatisfactory. In this paper, the authors conduct an in-depth investigation into the underlying causes of this unsatisfactory generalization and conclude that the behavior features extracted by convolutional neural networks are intertwined with driver identity features. Based on this discovery, they propose a feature decomposition (FD) framework to disentangle these two types of features. The separated behavior features, referred to as driver-irrelevant behavior features, are subsequently leveraged for behavior recognition. Moreover, the authors introduce a co-training strategy to optimize the FD framework. This strategy enables behavior features and identity features to provide mutual auxiliary signals and encourages each other to drop the information that do not belong to them, so that the learned behavior features can be driver-irrelevant. Rigorous experiments are conducted on two widely-studied datasets, yielding results that demonstrate the superior performance of this framework and its improved generalization capabilities. Importantly, the framework’s fast inference capabilities make it highly suitable for real-world scenarios. Codes are released at https://github.com/gaohangcodes/ LearningDriverIrrelevantFeatures4DBR.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2024, IEEE.
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Authors:
- Gao, Hang
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0000-0003-4797-3072
- Hu, Mengting
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0000-0003-1536-5400
- Liu, Yi
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0000-0003-1399-7420
- Publication Date: 2024-10
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 14115-14127
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 25
- Issue Number: 10
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Automobile drivers; Data preparation; Detection and identification system applications; Distracted driving; Driving behavior; Machine learning
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;
Filing Info
- Accession Number: 01942208
- Record Type: Publication
- Files: TRIS
- Created Date: Jan 13 2025 10:24AM