Integrated automotive safety system design by a fuzzy neural network Online publication date: Fri, 25-Nov-2005
by Yi-Jen Mon
International Journal of Vehicle Autonomous Systems (IJVAS), Vol. 3, No. 2/3/4, 2005
Abstract: This paper proposes an integrated automotive safety system (IASS) design concept and control algorithm. The IASS has three main parts; a laser radar detection device (LRDD), a collision avoidance warning system (CAWS) using fuzzy neural network (FNN) algorithms to calculate a safe distance, and a braking model (BM) used to calculate a risk distance. By coordinating these three parts, an optimal fuel throttle level is generated to control the vehicle velocity to avoid collisions. Furthermore, if the vehicle velocity exceeds the optimal value, an alarm signal will be generated to restrict the driver's driving actions, such as reducing the fuel throttle setting. Three scenarios are used to test the proposed IASS design methodology, and the simulation results demonstrate that the IASS design methodology can achieve satisfactory performance and robustness.
Online publication date: Fri, 25-Nov-2005
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