System Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

A Study of Driver Behavior Inference Model at Time of Lane Change using Bayesian Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Tezuka, S. ; Niigata Univ., Niigata ; Soma, H. ; Tanifuji, K.

Recent years have brought hope that driving support systems tailored to the characteristics of each driver can be developed. To accomplish this, a driver model must be constructed that considers the driver's psychological function when inferring driver behavior. This paper thus proposes a method to infer driver behavior by capturing time-series steering angle data at the time of lane change. The proposed method uses a static type conditional Gaussian model on Bayesian networks. By using this method, if the driver behavior of the subject and learned data nearness of features (norms) are below a certain level, it is possible to infer driver behavior with nearly 100% probability. Moreover, compared to the HMM models, this method reduces the rate of incorrect inference inclusion.

Published in:

Industrial Technology, 2006. ICIT 2006. IEEE International Conference on

Date of Conference:

15-17 Dec. 2006