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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhang Jun Zhu Zhong-xiang Song Zheng-he Mao En-rong |
| Copyright Year | 2008 |
| Description | Author affiliation: Coll. of Eng., China Agric. Univ., Beijing, China (Zhang Jun; Zhu Zhong-xiang; Song Zheng-he; Mao En-rong) |
| Abstract | Multi-source information fusion was introduced for the evaluation on driver fatigue which is divided into sub-systematic and systematic evaluation by integrating information from visual cues in common use and steering wheel behavior and vehicle¿s trajectory information. Neural network is combined with Dempster-Shafer evidence theory to finish character fusion and decision fusion. In the phrase of fusion, analytic hierarchy process (AHP) is applied to determine the basic weight, at the same time, considering the impact of data reliability on weight, the nonstatic weight is adapted the system to the current situation by the combination data reliability with basic weight. According to the simulation experiment on the drive simulator, compared with the single index, the adaptation of the evaluation method to monitor driver fatigue was more accurate, reliable and robust. |
| Starting Page | 151 |
| Ending Page | 155 |
| File Size | 310840 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424437467 |
| DOI | 10.1109/ISCSCT.2008.284 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-20 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | non-static weight Dempster-Shafer evidence theory Computational modeling Computerized monitoring monitoring driver fatigue Wheels Fatigue Educational institutions Agricultural engineering neural network multi-source information fusion Neural networks Brain modeling Frequency Robustness |
| Content Type | Text |
| Resource Type | Article |
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