We present a framework for the estimation of driver behavior at intersections, with applications to autonomous driving and vehicle safety. The framework is based on modeling the driver behavior and vehicle dynamics as a hybrid-state system (HSS), with driver decisions being modeled as a discrete-state system and the vehicle dynamics modeled as a continuous-state system. The proposed estimation method uses observable parameters to track the instantaneous continuous state and estimates the most likely behavior of a driver given these observations. This paper describes a framework that encompasses the hybrid structure of vehicle–driver coupling and uses hidden Markov models (HMMs) to estimate driver behavior from filtered continuous observations. Such a method is suitable for scenarios that involve unknown decisions of other vehicles, such as lane changes or intersection access. Such a framework requires extensive data collection, and the authors describe the procedure used in collecting and analyzing vehicle driving data. For illustration, the proposed hybrid architecture and driver behavior estimation techniques are trained and tested near intersections with exemplary results provided. Comparison is made between the proposed framework, simple classifiers, and naturalistic driver estimation. Obtained results show promise for using the HSS–HMM framework.
A Framework for Estimating Driver Decisions Near Intersections
IEEE Transactions on Intelligent Transportation Systems ; 15 , 2 ; 637-646
2014-04-01
1648828 byte
Article (Journal)
Electronic Resource
English
A Framework for Estimating Driver Decisions Near Intersections
Online Contents | 2014
|Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections
DOAJ | 2020
|Driver vision at intersections
Engineering Index Backfile | 1939
|Driver characteristics at intersections
Engineering Index Backfile | 1967
|Exploring Driver Error at Intersections
Transportation Research Record | 2015
|