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Title: A novel multi-sensors fusion framework based on Kalman Filter and neural network for AFS application
Authors: Liu, JF
Cheng, KWE 
Zeng, J
Keywords: Adaptive frontlight system
Fuzzy neutral network
Kalman Filter
Multi-sensors fusion
Issue Date: 2015
Publisher: SAGE Publications
Source: Transactions of the Institute of Measurement and Control, 2015, v. 37, no. 9, p. 1049-1059 How to cite?
Journal: Transactions of the Institute of Measurement and Control 
Abstract: An adaptive front light system (AFS) is put forward by the Society of Automotive Engineers and Economic Commission for Europe as a means of enhancing vehicular lighting. Traditionally, AFS can be divided into three parts: (1) a leveling subsystem to make lighting parallel to the road surface; (2) a swiveling subsystem to change light distribution along with the angle of the steering wheel; (3) a dimming subsystem to reduce or intensify the lighting. In this paper, a new hybrid multi-sensor fusion framework combining Kalman Filter with neural network is proposed to adjust two stepper motors controlling the vehicles headlights pitch and yaw. Kalman Filter as the frontend is used to deal with redundant sensor signals that are collected from sensors in the different places. Fuzzy Neutral Network as the backend is used to generate adjustment of leveling and swiveling angle through the integration of different type signals. An adaptive parameter adjustment is accomplished by the proposed fusion framework with the varying filter coefficients. The simulation and experiment of leveling angle are conducted using the predefined experimental data. The evaluation results of leveling angle prove that the proposed algorithm can effectively filter out high-frequency perturbations and provide reliable outputs for stepper motor. The same results can be obtained for a swiveling subsystem. Consequently, the hybrid fusion framework is a feasible approach for AFS design to accomplish data processing and nonlinear mapping.
ISSN: 0142-3312
EISSN: 1477-0369
DOI: 10.1177/0142331214555213
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