Title page for ETD etd-07282011-000937


Type of Document Master's Thesis
Author Chong, Linsen
Author's Email Address linsenc@vt.edu
URN etd-07282011-000937
Title MODELING NATURALISTIC DRIVER BEHAVIOR IN TRAFFIC USING MACHINE LEARNING
Degree Master of Science
Department Civil Engineering
Advisory Committee
Advisor Name Title
Abbas, Montasir M. Committee Chair
Pasupathy, Raghu Committee Member
Ramakrishnan, Naren Committee Member
Keywords
  • Driver behavior
  • car-following
  • machine learning
  • safety critical events
Date of Defense 2011-07-26
Availability unrestricted
Abstract
This research is focused on driver behavior in traffic, especially during car-following situations and safety critical events. Driving behavior is considered as a human decision process in this research which provides opportunities for an artificial driver agent simulator to learn according to naturalistic driving data. This thesis presents two mechine learning methodologies that can be applied to simulate driver naturalistic driving behavior including risk-taking behavior during an incident and lateral evasive behavior which have not yet been captured in existing literature. Two special machine learning approaches Backpropagation (BP) neural network and Neuro-Fuzzy Actor Critic Reinforcement Learning (NFACRL) are proposed to model driver behavior during car-following situation and safety critical events separately. In addition to that, as part of the research, state-of-the-art car-following models are also analyzed and compared to BP neural network approach. Also, driver heterogeneity analyzed by NFACRL method is discussed. Finally, it presents the findings and limitations drawn from each of the specific issues, along with recommendations for further research.
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