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Sensors 2015, 15(9), 24191-24213; doi:10.3390/s150924191

A Self-Adaptive Dynamic Recognition Model for Fatigue Driving Based on Multi-Source Information and Two Levels of Fusion

1
School of Information and Control, Nanjing University of Information Science & Technology, Nanjing 210044, China
2
School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
3
School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
4
The NEXTRANS Center, Purdue University, West Lafayette, IN 47906, USA
5
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
6
School of Transportation, Southeast University, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 13 August 2015 / Revised: 11 September 2015 / Accepted: 11 September 2015 / Published: 18 September 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [4194 KB, uploaded 18 September 2015]   |  

Abstract

To improve the effectiveness and robustness of fatigue driving recognition, a self-adaptive dynamic recognition model is proposed that incorporates information from multiple sources and involves two sequential levels of fusion, constructed at the feature level and the decision level. Compared with existing models, the proposed model introduces a dynamic basic probability assignment (BPA) to the decision-level fusion such that the weight of each feature source can change dynamically with the real-time fatigue feature measurements. Further, the proposed model can combine the fatigue state at the previous time step in the decision-level fusion to improve the robustness of the fatigue driving recognition. An improved correction strategy of the BPA is also proposed to accommodate the decision conflict caused by external disturbances. Results from field experiments demonstrate that the effectiveness and robustness of the proposed model are better than those of models based on a single fatigue feature and/or single-source information fusion, especially when the most effective fatigue features are used in the proposed model. View Full-Text
Keywords: fatigue driving; multi-source information; correlation analysis; fuzzy neural network; evidence theory fatigue driving; multi-source information; correlation analysis; fuzzy neural network; evidence theory
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sun, W.; Zhang, X.; Peeta, S.; He, X.; Li, Y.; Zhu, S. A Self-Adaptive Dynamic Recognition Model for Fatigue Driving Based on Multi-Source Information and Two Levels of Fusion. Sensors 2015, 15, 24191-24213.

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