Extensible platform for studying the behavior of drivers in urban traffic

扫码查看

原文链接

NETL

外文摘要：Research on traffic management and automation focuses on macro-scale problems involving large number of vehicles。 It seems that the influence of individual decisions of particular drivers has not received sufficient attention over last decades。 Understanding the characteristics of particular drivers is crucial for traffic optimization and for decisions making process performed by automated vehicles。 In this paper we would like to present a prototype platform for studying characteristics of individual human drivers。 The platform collects data during driving in urban environment and performs analysis and classification of observed behaviors。 It is extensible in terms of measured parameters and classification methods。 We also present results of first experiment using the created platform - classification of drivers during the left turn maneuver。

外文关键词：

behavioural sciencesdecision makingdriver information systemsoptimisationroad vehiclesautomated vehicledecision making processdriver behaviorindividual human drivermacro-scale problemsobserved behaviortraffic managementtraffic optimizationurban environmenturban trafficAdaptation modelsAnalytical modelsData miningData modelsFeature extractionGlobal Positioning SystemVehicles

作者：

Blaszczyk, P.、Turek, W.、Cetnarowicz, K.

展开 >

作者单位：

AGH Univ. of Sci. & Technol., Krakow, Poland

会议名称：

IEEE International Conference on Intelligent Transportation Systems

会议地点：

Qingdao(CN)

会议母体文献：

2014 IEEE 17th International Conference on Intelligent Transportation Systems

页码：

1359-1362

出版时间：

2014

DOI：

10.1109/ITSC.2014.6957876