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

Sensors

Wei Sun¹  Xiaorui Zhang⁴  Srinivas Peeta³  Xiaozheng He²  Yongfu Li²  Senlai Zhu² 

[1] School of Information and Control, Nanjing University of Information Science & Technology, Nanjing 210044, China;The NEXTRANS Center, Purdue University, West Lafayette, IN 47906, USA; E-Mails:;School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA; E-Mail:;School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China; E-Mail:

关键词: fatigue driving;    multi-source information;    correlation analysis;    fuzzy neural network;    evidence theory;   

DOI  :  10.3390/s150924191

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.