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Multi-agent and driving behavior based rear-end collision alarm modeling and simulating
Authors:Jun Liang  Long Chen  Xian-yi Cheng  Xian-bo Chen
Affiliation:1. School of Computer Science and Telecommunication Engineering, JiangSu University, Zhenjiang 212013, China;2. School of Automobile and Traffic Engineering, JiangSu University, Zhenjiang 212013, China;1. DeVry University, Chicago Campus, 3300 North Campbell Avenue, Chicago 60618, USA;2. ICube, University of Strasbourg, CNRS, UMR 7537, 300 Boulevard Sébastien Brant, BP 10413, 67412 Illkirch Cedex, France;1. Graduate School of Information Systems, University of Electro-Communications, Chofu-shi, Tokyo, Japan;2. Information Systems Architecture Science Research Division, National Institute of Informatics, Chiyoda-ku, Tokyo, Japan;3. School of Electronics and Information Engineering, Tongji University, Shanghai, PR China;1. State Key Lab of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China;2. Zhejiang Wanli University, Ningbo, China;3. Institute of Software, Chinese Academy of Sciences, Beijing, China;4. University of Thessaly, Volos, Greece;1. Division of Electrical & Computer Engineering, School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA 70803, United States;2. Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, United States;3. Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA 70803, United States
Abstract:Distance-between-vehicle-measurement is the only factor in traditional car rear-end alarm system. To address the above problem, this paper proposes an alarming model based on multi-agent systems (MAS) and driving behavior. It consists of four different types of agents that can either work alone or collaborate through a communications protocol on the basis of the extended KQML. The rear-end alarming algorithm applies the Bayes decision theory to calculate the probability of collision and prevent its occurrence real-time. The learning algorithm of driving behavior based on ensemble artificial neural network (ANN) and the decision procedure based on Bayes’ theory are also described in this paper. Both autonomy and reliability are enhanced in the proposed system. The effectiveness and robustness of the model have been confirmed by the simulated experiments.
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