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Driver Modeling based on computational intelligence approaches: exploaration and Modeling driver-following data collected by an instrumented vehicle
KTH, School of Architecture and the Built Environment (ABE), Transport and Economics.
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: urn:nbn:se:kth:diva-4253ISBN: 978-91-85539-11-6 (print)ISBN: 91-85539-11-2 (print)OAI: oai:DiVA.org:kth-4253DiVA: diva2:11477
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
List of papers
1. Behavior measurement, analysis and regime classification in car following
Open this publication in new window or tab >>Behavior measurement, analysis and regime classification in car following
2007 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 8, no 1, 144-156 p.Article in journal (Refereed) Published
Abstract [en]

This paper first reports a data acquisition method that the authors used in a project on modeling driver behavior for microscopic traffic simulations. An advanced instrumented vehicle was employed to collect driver-behavior data, mainly car-following and lane-changing patterns, on Swedish roads. To eliminate the measurement noise in acquired car-following patterns, the Kalman smoothing algorithm was applied to the state-space model of the physical states (acceleration, speed, and position) of both instrumented and tracked vehicles. The denoised driving patterns were used in the analysis of driver properties in the car-following stage. For further modeling of car-following behavior, we developed and implemented a consolidated fuzzy clustering algorithm to classify different car-following regimes from the preprocessed data. The algorithm considers time continuity of collected driver-behavior patterns and can be more reliably applied in the classification of continuous car-following regimes when the classical fuzzy C-means algorithm gives unclear results.

Place, publisher, year, edition, pages
IEEE Press, 2007
Keyword
car-following regime classification; driver behavior; fuzzy clustering algorithms; instrumented vehicle; Kalman smoothing; MODELS; SIMULATION
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-6691 (URN)10.1109/TITS.2006.883111 (DOI)000244929200016 ()2-s2.0-33847763742 (Scopus ID)
External cooperation:
Note

QC 20100602

Available from: 2006-12-29 Created: 2006-12-29 Last updated: 2016-09-05Bibliographically approved
2. Driver reaction delay estimation from real data and its application in GM-type model evaluation
Open this publication in new window or tab >>Driver reaction delay estimation from real data and its application in GM-type model evaluation
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.

Keyword
Car following data, reaction time estimation, spectrum analysis methods, GM-type model calibration and closed-loop simulation
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-6692 (URN)
External cooperation:
Note

QC 20160905

Available from: 2006-12-29 Created: 2006-12-29 Last updated: 2016-09-05Bibliographically approved
3. Statistical analysis of driver behavioral data in different regimes of the car-following stage
Open this publication in new window or tab >>Statistical analysis of driver behavioral data in different regimes of the car-following stage
2007 (English)In: Transportation Research Record, ISSN 0361-1981, no 2018, 87-96 p.Article in journal (Refereed) Published
Abstract [en]

An instrumented vehicle has been used to study car-following behavior on Swedish motorways. In this study, the previous data collection and pre-processing work were briefly reviewed. To understand the driving behavior in the car-following stage more clearly, the collected time series were classified into a number of regimes using unsupervised fuzzy clustering methods. Then, the statistical relations between the driver acceleration response and the perceptual variables in each regime were analyzed using correlation and regression methods. It was found that regime classification helps discern the behavioral variance between those regime clusters. According to the data analysis, some of the car-following regimes, for example, opening and braking, can be described adequately in the statistical sense by a linear regression model (Helly's model). Therefore, a multiple regime car-following model with simple model forms, for example, linear models, has the potential to robustly represent the general car-following behavior in most regimes.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-6693 (URN)10.3141/2018-12 (DOI)000253232200012 ()2-s2.0-40449123503 (Scopus ID)
Note
QC 20100602Available from: 2006-12-29 Created: 2006-12-29 Last updated: 2011-11-07Bibliographically approved
4. A general Kalman-filter based model estimation method for car-following dynamics in traffic simulation
Open this publication in new window or tab >>A general Kalman-filter based model estimation method for car-following dynamics in traffic simulation
2006 (English)In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090XArticle in journal (Other academic) Submitted
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-6694 (URN)
Note
QS 20120316Available from: 2006-12-29 Created: 2006-12-29 Last updated: 2012-03-16Bibliographically approved
5. A computational model for driver-following behavior based on a neural-fuzzy system
Open this publication in new window or tab >>A computational model for driver-following behavior based on a neural-fuzzy system
2007 (English)Report (Other academic)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-6695 (URN)
Note

QC 20160518

Available from: 2006-12-29 Created: 2006-12-29 Last updated: 2016-05-18Bibliographically approved
6. Predicting the effect of various ISA penetration grades on pedestrian safety by simulation
Open this publication in new window or tab >>Predicting the effect of various ISA penetration grades on pedestrian safety by simulation
2005 (English)In: Accident Analysis and Prevention, ISSN 0001-4575, E-ISSN 1879-2057, Vol. 37, no 6, 1162-1169 p.Article in journal (Refereed) Published
Abstract [en]

Intelligent speed adaption (ISA) is one type of vehicle-based intelligent transportation systems (ITS), which warns and regulates driving speed according to the speed limits of the roads. Early field studies showed that ISA could reduce general mean speed levels and their variances in different road environments. This paper studies the effects of various ISA penetration grades on pedestrian safety in a single lane road. A microscopic traffic simulation tool, TPMA, was further developed and used to implement different ISA penetration grades. Momentary spot speed and traffic flow data are first logged in the traffic simulation for later prediction of pedestrian safety. Then a hypothetical vehicle-pedestrian collision model is extended from early researches in order to estimate two safety indicators: probability of collision, and risk of death. Finally, Monte Carlo method is applied iteratively to compute those safety indices. The computational result shows that raising ISA penetration in traffic flow will reduce both the probability of mid-block collision between vehicle and pedestrian and the risk of death in the collision accidents. Furthermore, the decrease of the risk of death will be more prominent than that of the collision probability according to this method.

Keyword
ISA penetration; pedestrian safety; collision model; TPMA simulation; Monte Carlo experiment
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-6696 (URN)10.1016/j.aap.2005.06.017 (DOI)000233183200022 ()2-s2.0-26944492180 (Scopus ID)
Note
QC 20100602Available from: 2006-12-29 Created: 2006-12-29 Last updated: 2011-11-10Bibliographically approved

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