Driver anomaly quantification for intelligent vehicles: a contrastive learning approach with representation clustering

• Main Authors: Hu, Zhongxu, Xing, Yang, Gu, Weihao, Cao, Dongpu, Lv, Chen
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Mechanical engineering; Driver Anomaly; Online Quantification
• Online Access: https://hdl.handle.net/10356/164361
• Institution: Nanyang Technological University

Driver anomaly quantification is a fundamental capability to support human-centric driving systems of intelligent vehicles. Existing studies usually treat it as a classification task and obtain discrete levels for abnormalities. Meanwhile, the existing data-driven approaches depend on the quality of...