Efficient Driver Anomaly Detection via Conditional Temporal Proposal and Classification Network-Reference-Cited by-同舟云学术

Efficient Driver Anomaly Detection via Conditional Temporal Proposal and Classification Network

Published:2023-04 Issue:2 Volume:10 Page:736-745
ISSN:2329-924X
Container-title:IEEE Transactions on Computational Social Systems
language:
Short-container-title:IEEE Trans. Comput. Soc. Syst.

Author:

Su Lang¹^ORCID,Sun Chen¹^ORCID,Cao Dongpu²,Khajepour Amir¹^ORCID

Affiliation:

1. Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada

2. School of Vehicle and Mobility, Tsinghua University, Beijing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Human-Computer Interaction,Social Sciences (miscellaneous),Modeling and Simulation

Link

http://xplorestaging.ieee.org/ielx7/6570650/10089892/09740534.pdf?arnumber=9740534

Reference53 articles.

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2. Driver Anomaly Detection: A Dataset and Contrastive Learning Approach

3. Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles

4. A novel public dataset for multimodal multiview and multispectral driver distraction analysis: 3MDAD

5. Modified supervised contrastive learning for detecting anomalous driving behaviours;khan;arXiv 2109 04021,2021

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