抄録
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.
| 元の言語 | English |
|---|---|
| ジャーナル | SAE International Journal of Passenger Cars - Mechanical Systems |
| 巻 | 7 |
| 発行部数 | 3 |
| DOI | |
| 出版物ステータス | Published - 2014 |
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ASJC Scopus subject areas
- Automotive Engineering
- Mechanical Engineering
- Safety, Risk, Reliability and Quality
- Modelling and Simulation
これを引用
A Study on Modeling of Driver's Braking Action to Avoid Rear-End Collision with Time Delay Neural Network. / Hirose, Toshiya; Gokan, Masato; Kasuga, Nobuyo; Sawada, Toichi.
：: SAE International Journal of Passenger Cars - Mechanical Systems, 巻 7, 番号 3, 2014.研究成果: Article
}
TY - JOUR
T1 - A Study on Modeling of Driver's Braking Action to Avoid Rear-End Collision with Time Delay Neural Network
AU - Hirose, Toshiya
AU - Gokan, Masato
AU - Kasuga, Nobuyo
AU - Sawada, Toichi
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=84903390306&partnerID=8YFLogxK
U2 - 10.4271/2014-01-0201
DO - 10.4271/2014-01-0201
M3 - Article
AN - SCOPUS:84903390306
VL - 7
JO - SAE International Journal of Passenger Cars - Mechanical Systems
JF - SAE International Journal of Passenger Cars - Mechanical Systems
SN - 1946-3995
IS - 3
ER -