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A Study on Modeling of Driver's Braking Action to Avoid Rear-End Collision with Time Delay Neural Network
ISSN: 1946-3995, e-ISSN: 1946-4002
Published April 01, 2014 by SAE International in United States
Citation: Hirose, T., Gokan, M., Kasuga, N., and Sawada, T., "A Study on Modeling of Driver's Braking Action to Avoid Rear-End Collision with Time Delay Neural Network," SAE Int. J. Passeng. Cars - Mech. Syst. 7(3):1016-1026, 2014, https://doi.org/10.4271/2014-01-0201.
Collision avoidance systems for rear-end collisions have been researched and developed. It is necessary to activate collision warnings and automatic braking systems with appropriate timing determined by a monitoring system of a driver's braking action. Although there are various systems to monitor driving behavior, this study aims to create a monitoring system using a driver model. This study was intended to construct a model of a driver's braking action with the Time Delay Neural Network (TDNN).
An experimental scenario focuses on rear-end collisions on a highway, such as the driver of a host vehicle controlling the brake to avoid a collision into a leading vehicle in a stationary condition caused by a traffic jam. In order to examine the accuracy of the TDNN model, this study used four parameters: the number of learning, the number of neurons in the hidden layer, the sampling time with 0.01 second as a minimum value, and the number of the delay time. In addition, this study made a comparative review of the TDNN model and the Neural Network (NN) model to examine the accuracy of the TDNN model.
It was found that (1) TDNN allows for establishing a model with higher repeatability of a driver's driving action, (2) when comparing with NN, the accuracy of the model was improved for TDNN, and (3) even if a driver repeatedly presses the brake pedal in a short time, TDNN can accurately simulate a complex braking action by a driver.
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