@article{ref1,
title="Integrated driver modelling considering state transition feature for individual adaptation of driver assistance systems",
journal="Vehicle system dynamics",
year="2010",
author="Raksincharoensak, Pongsathorn and Khaisongkram, Wathanyoo and Nagai, Masao and Shimosaka, Masamichi and Mori, Taketoshi and Sato, Tomomasa",
volume="48",
number="Suppl 1",
pages="55-71",
abstract="This paper describes the modelling of naturalistic driving behaviour in real-world traffic scenarios, based on driving data collected via an experimental automobile equipped with a continuous sensing drive recorder. This paper focuses on the longitudinal driving situations which are classified into five categories - car following, braking, free following, decelerating and stopping - and are referred to as driving states. Here, the model is assumed to be represented by a state flow diagram. Statistical machine learning of driver-vehicle-environment system model based on driving database is conducted by a discriminative modelling approach called boosting sequential labelling method.<p />",
language="en",
issn="0042-3114",
doi="10.1080/00423111003668229",
url="http://dx.doi.org/10.1080/00423111003668229"
}