Generation of Pedal Operation Patterns of Individual Drivers in Car-Following for Personalized Cruise Control

• Nishiwaki, Yoshihiro

• Miyajima, Chiyomi

• Kitaoka, Norihide

• Itou, Katsunobu

• Takeda, Kazuya

Abstract

This paper presents a method to generate car-following patterns for individual drivers. We assume that driving is a recursive process. A driver recognizes a road environment such as velocity and following distance and adjusts gas and brake pedal positions. A vehicle status changes according to the driver's operation and the road environment changes according to the vehicle status. Driving patterns of each driver are modeled with a Gaussian mixture model (GMM), which is trained as a joint probability distribution of following distance, velocity, pedal position signals and their dynamics. Gas and brake pedal operation patterns are generated from the GMMs in a maximum likelihood criterion so that the conditional probability is maximized for a given environment i.e., following distance and velocity. Experimental results for a driving simulator show that car-following patterns generated from GMMs for three different drivers maintain their individual driving characteristics.

Journal

• IEEE Intelligent Vehicles Symposium

  IEEE Intelligent Vehicles Symposium 823-827, 2007

  IEEE

Details

• CRID: 1050282813773668352
• NII Article ID: 120000976060
• HANDLE: 2237/9597
• ISSN: 19310587
• Web Site: https://nagoya.repo.nii.ac.jp/records/7875
• Text Lang: en
• Article Type: journal article
• Data Source:
  • IRDB
  • CiNii Articles