Anomaly Detection and Fault Diagnosis Method for Autonomous Transport Vehicles on Unstructured Roads

• Published in: IEEE International Conference on Industrial Informatics (INDIN) pp. 1 - 7
• Main Authors: Zhang, Yifang, Yu, Guizhen, Li, Han, Zhang, Chaoqi, Li, Lecong, Zhang, Chuanying
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.08.2024
• Subjects: Accuracy; Anomaly detection; Autonomous vehicles; extreme value theory; Fault diagnosis; KL divergence; lateral deviation; Logic; Mining autonomous driving; Production; Roads; Safety; Transportation
• DOI: 10.1109/INDIN58382.2024.10774522

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%.