A framework of fault detection algorithm for intelligent vehicle by using real experimental data
A framework of fault detection algorithm for intelligent vehicle by using real experimental data
复制标题
基于真实实验数据的智能汽车故障检测算法框架
DOI：
10.1109/itsc.2011.6082877
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
S. Tsugawa
中科院分区：
文献类型：
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作者：
N. Hashimoto;Ü. Özgüner;N. Sawant;Masashi Yokozuka;S. Kato;O. Matsumoto;S. Tsugawa
自动驾驶汽车有可能为运输提供更多的容量，安全性，低排放和高效率。然而，自动化系统的不稳定状态可能会导致严重的问题，因此自动化车辆需要高可靠性。本研究的目的是开发自动驾驶汽车的故障（不稳定状态）检测算法，并提高系统的整体可靠性。本研究通过在真实的世界中的实验，初步解决并更新了在正常和不稳定条件下的一些数据星座模式的识别。多次实验在公共区域（路线距离约为1.1[km]）进行，有时，一些行人，自行车和其他机器人共存。由于真实的实验环境中存在随机噪声，故障检测方法必须对环境的变化具有鲁棒性，为了提高正确检测故障的可靠性，采用马氏距离、相关系数和线性化方法进行故障检测。本研究的特点是利用真实的实验结果，构造算法并对其进行评估，并利用真实的实验数据进行仿真，仿真结果表明，所提出的系统能够正确地检测出故障，证明了所提出方法的有效性。
Automated vehicles have a possibility to contribute more capacity, safety, low emission and high efficiency to transportation. However, unstable conditions of the automated system can cause serious problem, thus the automated vehicle requires high reliability. The objective of this research is to develop algorithms of fault (unstable condition) detection for automated vehicles, and to improve the overall reliability of the system. In this study, we initially solved and updated identification of some pattern of data constellations under normal and unstable conditions through the experiments in a real world. The multiple experiments were done in the public area (course distance is about 1.1[km]) with some times, where some pedestrian, bicycles and other robots coexisted. The method of detecting faults utilizes mahalanobis distance, correlation coefficient and linearization in order to improve the reliability of detecting the faults correctly, because real experimental conditions include some random noises, and the method must be robust for the changing conditions. The feature of this study is to utilize the experiment results in real world, construct the algorithms and evaluate it. The simulations were done with the real experimental data, in order to evaluate it. The simulation result shows that the proposed system detects faults correctly, and it proves the validity of the proposed method proved.
基于多模型的移动机器人内部传感器故障检测与诊断
DOI：
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发表时间：
2003
期刊：
Proceeding of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (CD-ROM)
影响因子：
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作者：
Masafumi Hashimoto
通讯作者：
Masafumi Hashimoto