Intelligent Modeling of Human Driver: A Survey

Delice, I.I.; Ertuğrul, Şeniz

doi:10.1109/ivs.2007.4290189

Cited by 17 publications

(8 citation statements)

References 23 publications

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“…However, this type of model does not currently exist with the fidelity required to investigate the real-world fuel economy of individual vehicles over a range of routes with a range of driver characteristics. The caution of Delice and Ertugrul [33] in reporting that a driver model including all the parameters of a human driver would be expected to have 10 000-50 000 rules that should be identified.…”

Section: Discussionmentioning

confidence: 99%

Development of a driver model to study the effects of real-world driver behaviour on the fuel consumption

McGordon

Poxon

Cheng

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part D:

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The real-world fuel economy of vehicles is becoming increasingly important to manufacturers and customers. One of the major influences in this is driver behaviour, but it is difficult to study in a controlled and repeatable manner. An assessment of driver models for studying real-world driver behaviour has been carried out. It has been found that none of the currently existing driver models has sufficient fidelity for studying the effects of real-world driver behaviour on the fuel economy of the individual vehicle. A decision-making process has been proposed which allows a driver model with a range of driving tasks to be developed. This paper reports the initial results of a driver model as applied to the conceptually straightforward scenario of high-speed cruising. Data for the driver model have been obtained through real-world data logging. It has been shown that the simulation driver model can provide a good representation of real-world driving behaviour in terms of the vehicle speed, and this is compared with a number of logged driver speed traces. A comparison of the modelled fuel economy for logged and driver model real-world drivers shows good agreement.

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Section: Discussionmentioning

confidence: 99%

Development of a driver model to study the effects of real-world driver behaviour on the fuel consumption

McGordon

Poxon

Cheng

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part D:

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show abstract

“…Most relevant to the present study is the modeling of human driver behavior in the automotive domain. 6,31,33 From transfer function models to nonlinear and adaptive controllers, 6,33 there are myriad methodologies for modeling vehicle lateral control (steering), longitudinal control (acceleration and braking), and combined control. These models can be used to simulate human drivers when testing new vehicle designs and technologies, 6,33 and despite the complexity of human behavior, low order models are often sufficient for many control tasks.…”

Section: Control Theoretic Modelsmentioning

confidence: 99%

“…6,31,33 From transfer function models to nonlinear and adaptive controllers, 6,33 there are myriad methodologies for modeling vehicle lateral control (steering), longitudinal control (acceleration and braking), and combined control. These models can be used to simulate human drivers when testing new vehicle designs and technologies, 6,33 and despite the complexity of human behavior, low order models are often sufficient for many control tasks. 34 In our prior work, we introduced 8 and performed a stability analysis 35 on a low-order model inspired by MacAdam 31 that can be used to simulate human steering behavior in a teleoperated navigation task in the presence of latency.…”

Section: Control Theoretic Modelsmentioning

confidence: 99%

Development and analysis of an operator steering model for teleoperated mobile robots under constant and variable latencies

Vozar¹,

Storms²,

Tilbury³

2016

Robotica

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SUMMARYLatency hinders a mobile robot teleoperator's ability to perform remote tasks. However, this effect is not well modeled. This paper develops a model for teleoperator steering behavior as a PD controller based on projected lateral displacement, which was tuned to reflect user performance determined by a 31-subject user study under constant and variable latency (having mean latencies between 0 and 750 ms). Additionally, we determined that operator performance under variable latency could be mapped to the expected performance of an equivalent constant latency. We then tested additional latency distributions in simulation and demonstrated equivalent steering performance among several different latency distributions.

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“…A fixed-order optimal control model based on MOCM was thus proposed to reduce the order while keeping the model optimality [72]. Nonlinear methods such as neural networks, neuro-fuzzy or adaptive models were employed as well to model the human actions [77][78][79][80][81]. The modeling of the human operator has been studied in the physiological perspective.…”

Section: Human Response Modelingmentioning

confidence: 99%

A tutorial on learning human welder's behavior: Sensing, modeling, and control

Liu

Zhang

2014

Journal of Manufacturing Processes

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Human welder's experiences and skills are critical for producing quality welds in manual GTAW process. Learning human welder's behavior can help develop next generation intelligent welding machines and train welders faster. In this tutorial paper, various aspects of mechanizing the welder's intelligence are surveyed, including sensing of the weld pool, modeling of the welder's adjustments and this model-based control approach. Specifically, different sensing methods of the weld pool are reviewed and a novel 3D vision-based sensing system developed at University of Kentucky is introduced. Characterization of the weld pool is performed and human intelligent model is constructed, including an extensive survey on modeling human dynamics and neuro-fuzzy techniques. Closed-loop control experiment results are presented to illustrate the robustness of the model-based intelligent controller despite welding speed disturbance. A foundation is thus established to explore the mechanism and transformation of human welder's intelligence into robotic welding system. Finally future research directions in this field are presented.

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Intelligent Modeling of Human Driver: A Survey

Cited by 17 publications

References 23 publications

Development of a driver model to study the effects of real-world driver behaviour on the fuel consumption

Development of a driver model to study the effects of real-world driver behaviour on the fuel consumption

Development and analysis of an operator steering model for teleoperated mobile robots under constant and variable latencies

A tutorial on learning human welder's behavior: Sensing, modeling, and control

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