阈值
计算机科学
异常检测
残余物
冗余(工程)
人工智能
自编码
模式识别(心理学)
故障检测与隔离
数据挖掘
深度学习
算法
执行机构
图像(数学)
操作系统
作者
Haigen Min,Yukun Fang,Xia Wu,Xiaoping Lei,Shixiang Chen,Rui Teixeira,Bing Zhu,Xiangmo Zhao,Zhijie Xu
标识
DOI:10.1016/j.eswa.2023.120002
摘要
Fault diagnosis for autonomous vehicles aims to provide available information about the operation status of the vehicle to avoid potential risks, and sensor data provide the observations of the system only when sensors are proven to function adequately. Therefore, in the present work a fault diagnosis framework for autonomous vehicles with sensor self-diagnosis is proposed. It uses a residual consistency checking algorithm based on sensor redundancy to detect and isolate failed sensors in sensor self-diagnosis. Then, the denoising shrinkage autoencoder (DSAE) is put forward to address anomaly detection, where a shrinkage block with soft thresholding is embedded into the denoising autoencoder for feature representation enhancement, improving the anomaly detection performance. Several experiments with data collected from an autonomous vehicle in a real test field are implemented, and the results show that the proposed residual consistency checking algorithm can effectively detect and isolate the failed sensor, and the DSAE achieves relatively the best anomaly detection performance in terms of AUC_ROC and F1-score compared with several other machine learning based anomaly detectors studied.
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