title: A Self-Adaptive Dynamic Recognition Model for Fatigue Driving Based on Multi-Source Information and Two Levels of Fusion.
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.
authors.affiliation: School of Information and Control, Nanjing University of Information Science & Technology, Nanjing 210044, China. wsun@nuist.edu.cn.|The NEXTRANS Center, Purdue University, West Lafayette, IN 47906, USA. wsun@nuist.edu.cn.
authors.forename: Wei
authors.initials: W
authors.lastname: Sun
authors.affiliation: School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China. xrzhang@nuist.edu.cn.
authors.forename: Xiaorui
authors.initials: X
authors.lastname: Zhang
authors.affiliation: School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA. peeta@purdue.edu.|The NEXTRANS Center, Purdue University, West Lafayette, IN 47906, USA. peeta@purdue.edu.
authors.forename: Srinivas
authors.initials: S
authors.lastname: Peeta
authors.affiliation: The NEXTRANS Center, Purdue University, West Lafayette, IN 47906, USA. seanhe@purdue.edu.
authors.forename: Xiaozheng
authors.initials: X
authors.lastname: He
authors.affiliation: The NEXTRANS Center, Purdue University, West Lafayette, IN 47906, USA. liyongfu@cqupt.edu.cn.|College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. liyongfu@cqupt.edu.cn.
authors.forename: Yongfu
authors.initials: Y
authors.lastname: Li
authors.affiliation: The NEXTRANS Center, Purdue University, West Lafayette, IN 47906, USA. senlai@seu.edu.cn.|School of Transportation, Southeast University, Nanjing 210096, China. senlai@seu.edu.cn.
authors.forename: Senlai
authors.initials: S
authors.lastname: Zhu
keywords: correlation analysis; evidence theory; fatigue driving; fuzzy neural network; multi-source information