Self-Coaching System Based on Recorded Driving Data: Learning From One's Experiences

This paper describes the development of a self-coaching system to improve driving behavior by allowing drivers to review a record of their own driving activity. By employing stochastic driver-behavior modeling, the proposed system is able to detect a wide range of potentially hazardous situations, which conventional event data recorders are not able to capture, including those involving latent risks, of which drivers themselves are unaware. By utilizing these automatically detected hazardous situations, our web-based system offers a user-friendly interface for drivers to navigate and review each hazardous situation in detail (e.g., driving scenes are categorized into different types of hazardous situations and are displayed with corresponding multimodal driving signals). Furthermore, the system provides feedback on each risky driving behavior and suggests how users can safely respond to such situations. The proposed system establishes a cooperative relationship between the driver, the vehicle, and the driving environment, leading to the development of the next generation of safety systems and paving the way for an alternative form of driving education that could further reduce the number of fatal accidents. The system's potential benefits are demonstrated through preliminary extensive evaluation of an on-road experiment, showing that safe-driving behavior can be significantly improved when drivers use the proposed system.

  • Record URL:
  • Availability:
  • Supplemental Notes:
    • Abstract reprinted with permission of IEEE.
  • Authors:
    • Takeda, K
    • Miyajima, C
    • Suzuki, T
    • Angkititrakul, P
    • Kurumida, K
    • Kuroyanagi, Y
    • Ishikawa, H
    • Terashima, R
    • Wakita, T
    • Oikawa, M
    • Komada, Y
  • Publication Date: 2012


  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01501393
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: Dec 17 2013 9:31AM