NDLI: A General Framework to Detect Unsafe System States From Multisensor Data Stream

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A General Framework to Detect Unsafe System States From Multisensor Data Stream

Content Provider	IEEE Xplore Digital Library
Author	Huazhong Ning Wei Xu Yue Zhou Yihong Gong Huang, T.S.
Copyright Year	2000
Abstract	This paper proposes a general framework for detecting unsafe states of a system whose basic real-time parameters are captured by multiple sensors. Our approach is to learn a danger-level function that can be used to alert the users of dangerous situations in advance so that certain measures can be taken to avoid the collapse. The main challenge to this learning problem is the labeling issue, i.e., it is difficult to assign an objective danger level at each time step to the training data, except at the collapse points, where a definitive penalty can be assigned, and at the successful ends, where a certain reward can be assigned. In this paper, we treat the danger level as an expected future reward (a penalty is regarded as a negative reward) and use temporal difference (TD) learning to learn a function for approximating the expected future reward, given the current and historical sensor readings. The TD learning obtains the approximation by propagating the penalties/rewards observable at collapse points or successful ends to the entire feature space following some constraints. This avoids the labeling issue and naturally allows a general framework to detect unsafe states. Our approach is applied to, but not limited to, the application of monitoring driving safety, and the experimental results demonstrate the effectiveness of the approach.
Sponsorship	IEEE Intelligent Transportation Systems Society
Starting Page	4
Ending Page	15
Page Count	12
File Size	718721
File Format	PDF
ISSN	15249050
Volume Number	11
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Labeling Computer networks Real time systems Sensor systems Safety Detectors Laboratories Training data Monitoring unsafe system state Driving safety labeling issue multisensor temporal difference (TD) learning
Content Type	Text
Resource Type	Article
Subject	Automotive Engineering Mechanical Engineering Computer Science Applications

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