Driver Behavior Modeling near Intersections Using Support ... - cefns
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Driver Behavior Modeling near Intersections Using Support ... - cefns
One-vs-One multi-class classification method is used to estimate the driver's ..... CONFUSION MATRIX FOR MULTI-CLASS SVM USING 30%. TESTING SET AT ...
2015 IEEE Intelligent Vehicles Symposium (IV) June 28 - July 1, 2015. COEX, Seoul, Korea
Seifemichael B. Amsalu1, Abdollah Homaifar1, Fatemeh Afghah1, Saina Ramyar1, and Arda Kurt2
Abstract! " to the development of advanced driver assistance systems that can assist the drivers in complex situations. Developing precise driver behavior models near intersections can considerably reduce the number of accidents at road intersections. In this study, the problem of driver behavior modeling near a road intersection is investigated using support vector machines (SVMs) based on the hybrid-state system (HSS) framework. In the HSS framework, the decisions of the driver are represented as a discrete-state system and the vehicle dynamics are represented as a continuous-state system. The proposed modeling technique utilizes the continuous observations from the vehicle and est " step using a multi-class SVM approach. Statistical methods are used to extract features from continuous observations. This allows for the use of history in estimating the current state. The developed algorithm is trained and tested successfully using naturalistic driving data collected from a sensor-equipped vehicle operated in the streets of Columbus, OH and provided by the Ohio State University. The proposed framework shows a promising accuracy of above 97% in estimating the driver's intention when approaching an intersection.
I. INTRODUCTION The development of autonomous vehicles leads to a mixed-traffic environment where human driven, semiautonomous and autonomous vehicles must cooperate to assure a safe traffic flow. This requires estimation and 9;.-2,=287 8/ =1. -;2?.;7.; IB+;2--state -;2?.;?.12,5. 68-.55270 .
