Driver Anomaly Quantification for Intelligent Vehicles: A Contrastive Learning Approach With Representation Clustering-Reference-Cited by-同舟云学术

Driver Anomaly Quantification for Intelligent Vehicles: A Contrastive Learning Approach With Representation Clustering

Author:

Hu Zhongxu¹^ORCID,Xing Yang²^ORCID,Gu Weihao³,Cao Dongpu⁴^ORCID,Lv Chen¹^ORCID

Affiliation:

1. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore

2. Centre for Autonomous and Cyber-Physical Systems, Cranfield University, Bedford, U.K.

3. Haomo.AI, Beijing, China

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

Funder

Agency for Science, Technology and Research

National Key Research and the Alibaba Group

Alibaba Innovative Research Program

Alibaba–Nanyang Technological University Joint Research Institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Automotive Engineering

Link

Reference53 articles.

3. Supervised contrastive learning;khosla;Adv Neural Inf Process Syst,2020

4. Understanding contrastive representation learning through alignment and uniformity on the hypersphere;wang;Proc Int Conf Mach Learn,0

5. Improved baselines with momentum contrastive learning;chen,2020

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