Driver Car-Following Behavior Modeling Using Neural Network Based On Real Traffic Experimental Data

This paper presents a method used to model the driver’s behavior during car-following situations. The model is designed with a Back-Propagation (BP) neural network to reproduce the host vehicle longitudinal accelerations according to the states of Time Headway and Time-to-Collision inverse. To obtain high-quality data in real traffic for the modeling work, the authors carried out experiments with an instrumented vehicle test bed resulting in extraction of steady state car-following scenarios. The data segments were processed with Kalman filter to eliminate the measurement noise and estimate the longitudinal acceleration for the network training. The simulations with different leading vehicle speed inputs were then conducted. Results show that the neural network model is capable of simulating the driver’s car-following behavior and can adapt to normal car-following situations. These systems are designed to facilitate a comfortable distance between lead and following cars and to reduce the driver's workload in terms of manual speed and distance control actions.

• Corporate Authors:

  ITS America

  1100 17th Street, NW, 12th Floor
  Washington, DC  United States  20036
• Authors:
  • Zhang, Lei
  • Wang, Jianqiang
  • Li, Keqiang
  • Yamamura, Tomohiro
  • Kuge, Nobuyuki
  • Nakagawa, Tsunehiko
• Conference:
  • 15th World Congress on Intelligent Transport Systems and ITS America's 2008 Annual Meeting
  • Location: New York NY, United States
  • Date: 2008-11-16 to 2008-11-20
• Publication Date: 2008

Language

• English

Media Info

• Media Type: CD-ROM
• Features: Figures; Photos; References;
• Pagination: 9p
• Monograph Title: ITS Connections: Saving Time. Saving Lives

Subject/Index Terms

• TRT Terms: Automobile driving; Autonomous intelligent cruise control; Behavior; Car following; Crash avoidance systems; Driver support systems; Following distance; Intelligent transportation systems; Neural networks
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment; I83: Accidents and the Human Factor; I91: Vehicle Design and Safety;

Filing Info

• Accession Number: 01140792
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 29 2009 8:57AM