Driver models in automobile dynamics application

Plöchl, Manfred; Edelmann, Johannes

doi:10.1080/00423110701432482

Cited by 257 publications

(167 citation statements)

References 92 publications

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“…More recent works use a virtual driving simulator to collect realistic driving data from human drivers and to model human driving behavior [18], or classify driving style by combining objective rank method with recurrent learning based on Elman's type neural network [20]. There are also related works on modelling traffic flow, driving course decisions, or drivers behavior in emergency situations [19]. However, very few studies explore the modeling of personal driving style of various vehicle drivers based on several driving parameters, especially in urban traffic [7][8][9].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Driving Style Analysis Using Data Mining Techniques

Constantinescu¹,

Cristian

Vlădoiu

2010

INT J COMPUT COMMUN

187

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This paper investigates the modeling of the personal driving style of various vehicle drivers based on several driving parameters. The purpose of such an endeavor is to classify the drivers according to their risk-proneness within the larger context of increasing traffic safety, which is a major concern worldwide. This information is valuable especially for those involved in fleet management and it can be used to improve and to make safer the driving style of various individuals who serve within that fleet. Equally important, such information could help any driver to see the danger within his or her driving style. Cluster and principal component analyses from exploratory statistics have been used to identify and explain drivers grouping according to their driving behavior. The driving parameters (behavioral indices) are collected from urban traffic by an in-house developed GPS-based device and sent to a data server for analysis.

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Section: Introduction and Related Workmentioning

confidence: 99%

Driving Style Analysis Using Data Mining Techniques

Constantinescu¹,

Cristian

Vlădoiu

2010

INT J COMPUT COMMUN

187

View full text Add to dashboard Cite

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“…The driver model also implements a path filter which incorporates the path angle and the rate of path angle change in the preview point. Aspects of this driver model are implemented in the commercial software veDYNA (Plöchl andEdelmann, 2007 andIrmscher andEhmann, 2004). The driver model has one tuning parameter which was tuned to give the best performance at 60 km/h.…”

Section: Implementation Of Resultsmentioning

confidence: 99%

“…An extensive overview of driver models for vehicle dynamics applications is given by Plöchl and Edelmann (2007). Many different driver models have been proposed, most of which have specific applications and are difficult to generalise for all requirements.…”

Section: Introductionmentioning

confidence: 99%

The relationship between vehicle yaw acceleration response and steering velocity for steering control

Thoresson

Botha

Schalk

2014

IJVD

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This paper proposes a novel concept for the modelling of a vehicle steering driver model for path following. The proposed steering driver reformulates and applies the Magic Formula, used for tyre modelling, to the vehicle's yaw acceleration vs. steering velocity response as a function of vehicle speed.The path-following driver model was developed for use in gradient-based mathematical optimisation of vehicle suspension characteristics for handling. Successful application of gradient-based optimisation depends on the availability of good gradient information. This requires a robust driver model that can ensure completion of the required handling manoeuvre, even when the vehicle handling is poor.The steering driver is applied to a non-linear full vehicle model of a Sports Utility Vehicle, performing a severe double lane change manoeuvre. Simulation results show excellent correlation with test results. The proposed driver model is robust and well suited to gradient-based optimisation of vehicle handling.

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“…Because these conditions were determined through trial and error, further adjustments are necessary to improve the performance of the detectors. vehicle and is based on the preview driver models [14]. Figure 15 shows the definitions of the variables used for yaw rate control and the equation for control is expressed as follows:…”

Section: Object Clustering and Cluster Matchingmentioning

confidence: 99%

Interactive Collision Avoidance Based on Surrounding Mobility Type for an Intelligent Powered Wheelchair

Ito

Kamata

2013

International Journal of Advanced Robotic Systems

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Electric powered wheelchairs have been developed for people with disabilities and the elderly who have difficulties walking and participating in social activities. However, for users with decreased cognitive abilities and judgment due to further aging, mobility aids that are not only powered but also intelligent are required. Intelligent powered wheelchairs with autonomous locomotion functions are one solution to this problem. This research focused on the complexity of coexisting ʺmobility typesʺ on sidewalks and a prototype intelligent wheelchair equipped with interactive strategies for collision avoidance based on the surrounding mobility type was developed. In order to classify the mobility type of coexisting traffic participants, image-processing components combined with LIDAR were developed. Based on the classification results, the wheelchair selected and executed either a changing-lanes strategy or deceleration strategy autonomously.

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Driver models in automobile dynamics application

Cited by 257 publications

References 92 publications

Driving Style Analysis Using Data Mining Techniques

Driving Style Analysis Using Data Mining Techniques

The relationship between vehicle yaw acceleration response and steering velocity for steering control

Interactive Collision Avoidance Based on Surrounding Mobility Type for an Intelligent Powered Wheelchair

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