Identification and Application of Neural Operator Models in a Car Driving Situation

Kraiss, Karl-Friedrich; Küttelwesch, Heinz

doi:10.1016/s1474-6670(17)50180-9

Cited by 11 publications

(3 citation statements)

References 2 publications

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“…This of course depends upon the driver having some basic understanding of the controlled vehicle dynamics -hence the internal vehicle model concept or its equivalent -in order to perform the projection. Neural network models [116][117][118][119][120][121][122][123][124][125][126] of the driving process essentially subsume many of these ideas within the neural net architecture and associated connection weights. Fuzzy logic [127] and genetic algorithm [128] approaches accomplish much the same.…”

Section: Internal Vehicle Model Conceptmentioning

confidence: 99%

“…Other examples of driver modeling efforts that have incorporated artificial intelligence methods such as neural networks, fuzzy logic, and genetic algorithms include the works of [117][118][119][120][121][122][123][124][125][126][127][147][148][149]. These latter modeling approaches essentially capture many of the key driver properties and characteristics noted above as part of their tuning methodology which typically involves network training, or iterative-like parameter adjustments, in order to match a desired or targeted driver control behavior.…”

Section: Modelling the Human Drivermentioning

confidence: 99%

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Understanding and Modeling the Human Driver

MacAdam¹

2003

Vehicle System Dynamics

398

215

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This paper examines the role of the human driver as the primary control element within the traditional driver-vehicle system. Lateral and longitudinal control tasks such as path-following, obstacle avoidance, and headway control are examples of steering and braking activities performed by the human driver. Physical limitations as well as various attributes that make the human driver unique and help to characterize human control behavior are described. Example driver models containing such traits and that are commonly used to predict the performance of the combined driver-vehicle system in lateral and longitudinal control tasks are identified.

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Section: Internal Vehicle Model Conceptmentioning

confidence: 99%

Section: Modelling the Human Drivermentioning

confidence: 99%

Understanding and Modeling the Human Driver

MacAdam¹

2003

Vehicle System Dynamics

398

215

View full text Add to dashboard Cite

show abstract

“…Other researchers have employed this technique to learn overtaking and lane-changing behaviour, predicting lateral and longitudinal vehicle movement, etc. [16][17][18]. Ou and Karray [19] use a deeplearning approach to predict lane change and turns prior to a green light.…”

Section: Related Work On Modelling Driver Behaviourmentioning

confidence: 99%

Predicting driver behaviour at intersections based on driver gaze and traffic light recognition

Rahman

Beauchemin

Bauer

2020

IET intell. transp. syst

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This work introduces and evaluates a model for predicting driver behaviour, namely turns or proceeding straight, at traffic light intersections from driver three-dimensional gaze data and traffic light recognition. Based on vehicular data, this work relates the traffic light position, the driver's gaze, head movement, and distance from the centre of the traffic light to build a model of driver behaviour. The model can be used to predict the expected driver manoeuvre 3 to 4 s prior to arrival at the intersection. As part of this study, a framework for driving scene understanding based on driver gaze is presented. The outcomes of this study indicate that this deep learning framework for measuring, accumulating and validating different driving actions may be useful in developing models for predicting driver intent before intersections and perhaps in other key-driving situations. Such models are an essential part of advanced driving assistance systems that help drivers in the execution of manoeuvres.

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Implementation of user-adaptive assistants with neural operator models

Kraiss

1995

Control Engineering Practice

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Identification and Application of Neural Operator Models in a Car Driving Situation

Cited by 11 publications

References 2 publications

Understanding and Modeling the Human Driver

Understanding and Modeling the Human Driver

Predicting driver behaviour at intersections based on driver gaze and traffic light recognition

Implementation of user-adaptive assistants with neural operator models

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