Modeling Driver Behavior at Intersections with Takagi-Sugeno Fuzzy Models

Due to the relatively high density of vehicles and humans at intersections, it is crucial for an Advanced Driver Assistance System (ADAS) to predict human driver behaviors to avoid crashes. Due to the complexity of a human's behavior interacting with a vehicle, it is very difficult to find an explicit model to analyze the driver's behavior. In this paper Takagi-Sugeno is used as a data driven technique to model and predict driver's behavior at intersections. In the proposed technique, a Takagi-Sugeno model is trained for each maneuver using a Gath-Geva clustering based algorithm. The proposed models are then evaluated with real time experimental data, and the estimation results are presented.

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Availability:
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Supplemental Notes:
- Abstract reprinted with permission of IEEE.
Corporate Authors:
Institute of Electrical and Electronics Engineers (IEEE)

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Authors:
- Ramyar, Saina
- Sefidmazgi, Mohammad Gorji
- Amsalu, Seifemichael
- Karimoddini, Ali
- Kurt, Arda
- Homaifar, Abdollah
Conference:
- 18th International IEEE Conference on Intelligent Transportation Systems (ITSC)
- Location: Canary Islands , Spain
- Date: 2015-9-15 to 2015-9-18
Publication Date: 2015

Language

English

Media Info

Media Type: Web
Features: References;
Pagination: pp 2378-2383
Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

TRT Terms: Algorithms; Behavior; Driver support systems; Drivers; Intersections
Uncontrolled Terms: Clustering; Fuzzy models
Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

Accession Number: 01601908
Record Type: Publication
ISBN: 9781467365956
Files: TRIS
Created Date: May 2 2016 3:24PM