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

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).

[1]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[2]  J. Goldbeck,et al.  Lane detection and tracking by video sensors , 1999, Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383).

[3]  Shorin Kyo,et al.  A robust vehicle detecting and tracking system for wet weather conditions using the IMAP-VISION image processing board , 1999, Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383).

[4]  E.M. El Koursi,et al.  Safety analysis of a frontal collision warning system , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[5]  Chung-Ping Young,et al.  Applying Embedded Hybrid ANFIS/Quantum-Tuned BPNN Prediction to Collision Warning System for Motor Vehicle Safety , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[6]  Mike McDonald,et al.  The effect of sensor errors on the performance of collision warning systems , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[7]  Dong Du,et al.  Battery state-of-charge (SOC) estimation using adaptive neuro-fuzzy inference system (ANFIS) , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[8]  Vishwesh V. Kulkarni,et al.  Development of a performance-based approach for a rear-end collision warning and avoidance system for automobiles , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[9]  Ming-Yang Chern,et al.  The lane recognition and vehicle detection at night for a camera-assisted car on highway , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[10]  Christoph Dürr,et al.  A Quantum Algorithm for Finding the Minimum , 1996, ArXiv.

[11]  Steven L. Potts,et al.  Forward-looking automotive radar sensor , 1995, Other Conferences.

[12]  Rongben Wang,et al.  A vision-based road edge detection algorithm , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[13]  Bao Rong Chang,et al.  NEW FORECASTING SCHEME USING QUANTUM MINIMIZATION TO REGULARIZE A COMPOSITE OF PREDICTION AND ITS NONLINEAR HETEROSCEDASTICITY , 2007 .

[14]  Han-Shue Tan,et al.  DGPS-Based Vehicle-to-Vehicle Cooperative Collision Warning: Engineering Feasibility Viewpoints , 2006, IEEE Transactions on Intelligent Transportation Systems.

[15]  Chih-Min Lin,et al.  Neural-network hybrid control for antilock braking systems , 2003, IEEE Trans. Neural Networks.

[16]  Lov K. Grover.  A fast quantum mechanical algorithm for database search , 1996, STOC '96.

[17]  G R Preziotti,et al.  ALERT ALGORITHM DEVELOPMENT PROGRAM: NHTSA REAR-END COLLISION ALERT ALGORITHM , 2002 .

[18]  O. Mano,et al.  Forward collision warning with a single camera , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[19]  M. Moallem,et al.  Optimal design of a hybrid controller for antilock braking systems , 2005, Proceedings, 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics..

[20]  Chakib Alaoui,et al.  On-board diagnostic and rejuvenation system for electric vehicles , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[21]  P. Ganci,et al.  A forward looking automotive radar sensor , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[22]  T. M. Kowalick.  Pros and cons of emerging event data recorders (EDRs) in the highway mode of transportation , 2001, IEEE VTS 53rd Vehicular Technology Conference, Spring 2001. Proceedings (Cat. No.01CH37202).