A Study on Modeling of Driver's Braking Action to Avoid Rear-End Collision with Time Delay Neural Network

• Published in: SAE International Journal of Passenger Cars - Mechanical Systems 2014-04, Vol.7 (3), p.1016-1026, Article 2014-01-0201
• Main Authors: Hirose, Toshiya, Gokan, Masato, Kasuga, Nobuyo, Sawada, Toichi
• Format: Article
• Language: English
• ISSN: 1946-3995; 1946-4002; 1946-4002
• DOI: 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.