NDLI: Agent-based evaluation of driver heterogeneous behavior during safety-critical events

Content Provider	IEEE Xplore Digital Library
Author	Abbas, M. Linsen Chong Higgs, B. Medina, A. Yang, C.Y.D.
Copyright Year	2011
Description	Author affiliation: Virginia Tech Signal Control & Operation Research and Education Systems Lab at Virginia Tech (Linsen Chong; Higgs, B.) \|\| Virginia Tech Transportation Institute (Medina, A.) \|\| Office of Operations R&D at Turner-Fairbank Highway Research Center (Yang, C.Y.D.) \|\| Virginia Tech (Abbas, M.)
Abstract	Heterogeneous driver behavior during safety-critical events is more complicated than normal driving situations and is difficult to capture by statistical models. This paper applies an agent-based reinforcement learning method to represent heterogeneous driving behavior for different drivers during safety-critical events. The naturalistic driving data of different drivers during safety-critical events are used in agent training. As an output of the Neuro-Fuzzy Actor Critic Reinforcement Learning (NFACRL) training technique, behavior rules are embedded in different agents to represent heterogeneous actions between drivers. The results show that the NFACRL is able to simulate naturalistic driver behavior and present heterogeneity.
Starting Page	1797
Ending Page	1802
File Size	839345
Page Count	6
File Format	PDF
ISBN	9781457721984
ISSN	21530009
e-ISBN	9781457721977
DOI	10.1109/ITSC.2011.6083089
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Acceleration Training Estimation Learning Databases
Content Type	Text
Resource Type	Article

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