By Topic

Neuro-fuzzy control of converging vehicles for automated transportation systems

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
$33 $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

2 Author(s)
Jahng-Hyon Park ; Sch. of Mech. Eng., Hanyang Univ., Seoul, South Korea ; Se-Hee Ryu

For an automated transportation system, like the PRT system or IVHS, an efficient vehicle-merging algorithm is required for smooth operation management of merging, collision avoidance between vehicles, ride comfort, and the effect on traffic should be considered. This paper proposes an unmanned vehicle merging algorithm that consists of two procedures. First, a longitudinal control algorithm is designed to keep a safe headway between vehicles in a single lane. Next, “vacant slot and ghost vehicle” concept is introduced and a decision algorithm is designed to determine the sequence of vehicles entering a converging section considering total traffic flow. The sequencing algorithm is based on fuzzy rules and the membership functions are determined first by an intuitive method and then trained by a learning method using neural net. The vehicle-merging algorithm is shown to be effective through simulations based on a PRT model

Published in:

American Control Conference, 1999. Proceedings of the 1999  (Volume:6 )

Date of Conference: