Intelligent data fusion system for predicting vehicle collision warning using vision/GPS sensing

Authors:
Bao Rong Chang;Hsiu Fen Tsai;Chung-Ping Young
Affiliations:
Department of Computer Science and Information Engineering, National University of Kaohsiung, 700, Kaohsiung University Road, Nanzih District, Kaohsiung 811, Taiwan;Department of International Business, Shu-Te University, 59, Hun Shang Road, Yen Chao, Kaohsiung County 824, Taiwan;Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan 1, University Road, Tainan City 701, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

In this study, fuzzy approach with fault-tolerance has proposed to fuse heterogeneous sensed data and overcome the problem of imprecise collision warning due to perturbed input signal when processing the pre-crash warning. Meanwhile, another problem relevant to the danger in drowsy driving, involving fatigue level, carbon monoxide concentration, and breath alcohol concentration, was considered and has approximately reasoned to an extra reaction time to modify NHTSA algorithm. A vision-sensing analysis cooperating with global-positioning system is applied for lane marking detection and collision warning, particularly exchanging the dynamic and static information between neighboring cars via inter-vehicle wireless communications. In addition to pre-crash warning, event data recording very useful for accident reconstruction on scene is also established here. In order to speed up data fusion on both quantum-tuned back-propagation neural network (QT-BPNN) and adaptive network-based fuzzy inference system (ANFIS), a distributed dual-platform DaVinci+XScale_NAV270 has been employed. Several tests on system's reliability and validity have been done successfully, and the comparison of system effectiveness showed that our proposed approach outperforms two current well-known collision-warning systems (AWS-Mobileye and ACWS-Delphi).