A rule-based neural network approach to model driver naturalistic behavior in traffic

This paper proposes a rule-based neural network model to simulate driver behavior in terms of longitudinal and lateral actions in two driving situations, namely car-following situation and safety critical events. A fuzzy rule based neural network is constructed to obtain driver individual driving rules from their vehicle trajectory data. A machine learning method reinforcement learning is used to train the neural network such that the neural network can mimic driving behavior of individual drivers. Vehicle actions by neural network are compared to actions from naturalistic data. Furthermore, this paper applies the proposed method to analyze the heterogeneities of driving behavior from different drivers’ data. Driving data in the two driving situations are extracted from Naturalistic Truck Driving Study and Naturalistic Car Driving Study databases provided by the Virginia Tech Transportation Institute according to pre-defined criteria. Driving actions were recorded in instrumented vehicles that have been equipped with specialized sensing, processing, and recording equipment.

• Record URL:
  https://doi.org/10.1016/j.trc.2012.09.011
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X12001210
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • Abstract reprinted with permission from Elsevier.
• Authors:
  • Chong, Linsen
  • Abbas, Montasir M
  • Medina Flintsch, Alejandra
  • Higgs, Bryan
• Publication Date: 2013-7

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 207-223
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 32
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Car following; Decision making; Driver performance; Fuzzy logic; Human machine systems; Neural networks; Rear end crashes
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; I72: Traffic and Transport Planning; I83: Accidents and the Human Factor;

Filing Info

• Accession Number: 01487668
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 17 2013 4:49PM