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Journal of Advanced Transportation
Volume 2017, Article ID 3080859, 11 pages
https://doi.org/10.1155/2017/3080859
Research Article

A Framework for Estimating Long Term Driver Behavior

1The Massachusetts Institute of Technology, Cambridge, MA, USA
2The Renaissance Computing Institute, Chapel Hill, NC 27517, USA
3The Ohio State University, Columbus, OH, USA

Correspondence should be addressed to Vijay Gadepally; moc.liamg@yllapedag.yajiv

Received 11 July 2016; Revised 16 October 2016; Accepted 27 October 2016; Published 12 January 2017

Academic Editor: William H. K. Lam

Copyright © 2017 Vijay Gadepally et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We present a framework for estimation of long term driver behavior for autonomous vehicles and vehicle safety systems. The Hybrid State System and Hidden Markov Model (HSS+HMM) system discussed in this article is capable of describing the hybrid characteristics of driver and vehicle coupling. In our model, driving observations follow a continuous trajectory that can be measured to create continuous state estimates. These continuous state estimates can then be used to estimate the most likely driver state using decision-behavior coupling inherent to the HSS+HMM system. The HSS+HMM system is encompassed in a HSS structure and intersystem connectivity is determined by using signal processing and pattern recognition techniques. The proposed method is suitable for a number of autonomous and vehicle safety scenarios such as estimating intent of other vehicles near intersections or avoiding hazardous driving events such as unexpected lane changes. The long term driver behavior estimation system involves an extended HSS+HMM structure that is capable of including external information in the estimation process. Through the grafting and pruning of metastates, the HSS+HMM system can be dynamically updated to best represent driver choices given external information. Three application examples are also provided to elucidate the theoretical system.