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Driver reaction delay estimation from real data and its application in GM-type model evaluation
KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
2006 (English)In: Transportation Research Record, ISSN 0361-1981, E-ISSN 2169-4052, no 1965, 130-141 p.Article in journal (Refereed) Published
Abstract [en]

Driver behavior plays an important role in modeling vehicle dynamics in a traffic simulation environment. To study one element of the general driver behavior, that of car following, an advanced instrumented vehicle has been applied in dynamic data collection in real traffic flow on Swedishroads. This paper briefly introduces our car following data collection and smoothing methods. Moreover, we introduce spectrum analysis methods based on Fourier analysis of car following data to estimate driver reaction times, a crucial parameter of driver behavior. As an example, we calibrate a generalized GM-type model, an extension of the classical nonlinear GM model, in stable following regime based on the estimated driver reaction times. The calibrated model is then evaluated by closed-loop simulations.

Place, publisher, year, edition, pages
2006. no 1965, 130-141 p.
Keyword [en]
Car following data, reaction time estimation, spectrum analysis methods, GM-type model calibration and closed-loop simulation
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-6692OAI: oai:DiVA.org:kth-6692DiVA: diva2:11472
Note

QC 20160905

Available from: 2006-12-29 Created: 2006-12-29 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Driver Modeling based on computational intelligence approaches: exploaration and Modeling driver-following data collected by an instrumented vehicle
Open this publication in new window or tab >>Driver Modeling based on computational intelligence approaches: exploaration and Modeling driver-following data collected by an instrumented vehicle
2006 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

This thesis is concerned with modeling of driver behavior based on data collected from real traffic using an advanced instrumented vehicle. In particular, the focus is on driver-following behavior (often called car-following in transport science) for microscopic simulation of road traffic systems. In addition, the modeling methodology developed can be applied for the design of human-centered control algorithms in adaptive cruise control (ACC) and other longitudinal active-safety technologies.

Driver behavior is a constant research topic in the modeling of traffic systems and Intelligent Transportation Systems (ITS), which could be traced back to the work of GeneralMotor (GM) Co. in 1950’s. In the early time, researchers were only interested in the development of driver models fulfilling basic physical properties and producing reasonable flow dynamics on a macroscopic level. With the booming interest on driver modeling on a microscopic level and needs in ITS developments, researchers now emphasize modeling using microscopic data acquired from real world. To follow this research trend, a methodological framework on car-following data acquisition, analysis and modeling has been developed step by step in this thesis, and the basic idea is to build a computational model for car-following behavior by exploration of collected data. To carry out the work, different techniques within the field of modern Artificial Intelligence (AI), namely Computational Intelligence (CI)1, have been applied in the research subtasks e.g. information estimation, behavioral regime classification, regime model integration and model estimation. Therefore, a preliminary introduction of the CI methods being used in this thesis work is included in the text.

Place, publisher, year, edition, pages
Stockholm: KTH, 2006. 81 p.
Series
Trita-TEC-PHD, ISSN 1653-4468 ; 06:004
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-4253 (URN)978-91-85539-11-6 (ISBN)91-85539-11-2 (ISBN)
Public defence
2007-01-19, Sal F3, KTH, Lindstedtsvägen 26, Stockholm, 13:00 (English)
Opponent
Supervisors
Note
QC 20100602Available from: 2006-12-29 Created: 2006-12-29 Last updated: 2010-06-02Bibliographically approved

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