@inproceedings{3354eb01f77545b89817d66cf00b381a,
title = "Anomaly Detection and Fault Diagnosis Method for Autonomous Transport Vehicles on Unstructured Roads",
abstract = "Autonomous vehicles in mining areas undertake substantial production tasks and are prone to various faults during operation. Early detection of abnormalities, along with timely fault warnings and diagnoses, can enhance transportation safety and increase vehicle turnout rates. This study utilizes driving data from autonomous vehicles in mining areas and considers the characteristics of unstructured road scenes. The driving area is segmented into distinct intervals, and Kullback-Leibler (KL) divergence is applied within each interval to detect anomalies in the vehicle's lateral deviation during operation. Experimental results demonstrate that the proposed method achieves an anomaly detection accuracy of 91.4\%, with a false negative rate of 8.3\% and a false positive rate of 8.7\%.",
keywords = "KL divergence, Mining autonomous driving, anomaly detection, extreme value theory, fault diagnosis, lateral deviation",
author = "Yifang Zhang and Guizhen Yu and Han Li and Chaoqi Zhang and Lecong Li and Chuanying Zhang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 22nd IEEE International Conference on Industrial Informatics, INDIN 2024 ; Conference date: 18-08-2024 Through 20-08-2024",
year = "2024",
doi = "10.1109/INDIN58382.2024.10774522",
language = "英语",
series = "IEEE International Conference on Industrial Informatics (INDIN)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2024 IEEE 22nd International Conference on Industrial Informatics, INDIN 2024",
address = "美国",
}