Modelling and Simulation of Human Behaviour in System Control

Cacciabue, Pietro Carlo

doi:10.1007/978-1-4471-1567-0

Cited by 76 publications

(36 citation statements)

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“…A model of cognition process for system control has been proposed [1] inspired by the human cognitive process. This model can be applied for solving all kinds of control tasks.…”

Section: Elements and The Corresponding Realization Of A Cognitive Frmentioning

confidence: 99%

Stabilization of unknown nonlinear systems using a cognition‐based framework

Shen

Zhang

Söffker

2011

Proc Appl Math and Mech

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This paper considers an adaptive control method based on a cognition-based framework to stabilize unknown nonlinear systems. In order to fulfill the task of stabilization, neither the information about the systems dynamical structure nor the knowledge about system physical behaviors, but the system states, which are assumed as measurable, are required. The structure of the proposed controller consists of three parts. The first part is based on a recurrent neural network (RNN) to be used for local identification of the unknown nonlinear system in real time. The network can be utilized as system characteristics, which is further used to design the controller within the third part. In the second part, the set of the given input values leading to stable behavior of the closed-loop system will be calculated numerically with a geometrical criterion based on a suitable definition of quadratic stability. In the third part, a suitable control input value is chosen accordingly to a time-relevant criteria from the set of input values generated in the second part of the controller. These three parts and their internal connections are arranged within a so-called cognition framework. The proposed cognitive controller is able to gain useful knowledge (with local validity) and define autonomously a suitable control input with respect to the requirements of the time-relevant criteria. Numerical examples are shown to demonstrate the successful application and performance of the method.

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“…A model of cognition process for system control has been proposed [1] inspired by the human cognitive process. This model can be applied for solving all kinds of control tasks.…”

Section: Elements and The Corresponding Realization Of A Cognitive Frmentioning

confidence: 99%

Stabilization of unknown nonlinear systems using a cognition‐based framework

Shen

Zhang

Söffker

2011

Proc Appl Math and Mech

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“…Other theories focus on the relationship between the driver's perceptual and motor processes and the driver's assessment of available time to execute maneuvers [18]- [21]. Available time could by reflected by TTC (Time To Collision) or TTLC (Time To Line Crossing, [22]).…”

Section: Technical System Efficiencymentioning

confidence: 99%

Dynamically deployed support as a potential solution to negative behavioral adaptation

Kovordanyi

Ohlsson²,

Alm³

2005

IEEE Proceedings. Intelligent Vehicles Symposium, 2005.

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Abstract-Advanced driver assistance systems are designed to make driving easier that is, to alleviate the driver's workload, and to increase traffic safety. However, traffic safety is affected by negative behavioral adaptation, meaning that drivers tend to increase speed and pay less attention to driving when supported by an advanced assistance system. We relate behavioral adaptation to reinforcement learning at a subconscious level, and propose that driver assistance is dynamically varied within predetermined safety limits. The aim of employing a dynamic assistance policy is to prevent the driver from noticing a constant improvement in vehicle handling. We conclude by describing ongoing work for empirically evaluating an improved lane departure warning system that uses a dynamic assistance policy.Index Terms-advanced driver assistance systems, lane departure warning systems, lane keeping assistance systems, negative behavioral adaptation, reinforcement learning, dynamic assistance policy I. INTRODUCTION he evolution of a new generation of advanced driver assistance systems is to a large extent propelled by advances in sensor technology, steadily increasing computing power and fast algorithms for analyzing in real-time multiple sources of sensor data [1]- [3]. Whereas much emphasis is put on the safety and reliability of the technical system, there is little concern of the human driver. However, the driver is of central importance for traffic safety. Empirical studies indicate that drivers have a tendency to adapt their driving style and misuse the increased safety margins created by advanced driver assistance systems, a phenomenon called negative behavioral adaptation [4]-[7]. Drivers may, for example, increase the driving speed and pay less attention to the driving task, to such an extent that the safety margins created by the driver assistance system are cancelled out [8], [9]. While this problem is widely acknowledged, to date no technically- based, engineering solutions have been proposed that could mitigate the adverse safety effects of negative behavioral adaptation.

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“…Furthermore the PIPE model (Fig. 4), developed by Cacciabue [5], has been used as a reference for a more detailed configuration of the operator. For the sake of brevity, the model, which is based on the four main cognitive functions-perception, interpretation, planning and execution-will not be described in detail in the paper.…”

Section: Cognitive Model Of the Operatormentioning

confidence: 99%

“…While the model of cognition is the theoretical representation of the mental processes and control actions developed by one or more operators during the execution of their tasks, given a physical system and a definite context [5].…”

Section: State Of the Art: Review Of Hra By Means Of Simulation Appromentioning

confidence: 99%

“…In the case of study the analysis has been carried out through the Multilevel Hazop [17] methodology and a related event tree (Fig. 3); a cognitive model of the operator based on SHEL [18] and PIPE [5] Human Hazop analysis (guide words); a taxonomy for the possible error type outlined in the recovery phase; a cognitive model for the evaluation of human behaviour in the recovery phase based on the framework proposed by Kontogiannis [19];…”

Section: Qualitativementioning

confidence: 99%

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A probabilistic cognitive simulator for HRA studies (PROCOS)

Trucco

Leva

2007

Reliability Engineering & System Safety

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The paper deals with the development of a simulator for approaching human errors in complex operational frameworks (e.g., plant commissioning). The aim is to integrate the quantification capabilities of the so-called 'first-generation' human reliability assessment (HRA) methods with a cognitive evaluation of the operator. The simulator allows analysing both error prevention and error recovery. It integrates cognitive human error analysis with standard hazard analysis methods (Hazop and event tree) by means of a 'semi static approach'. The comparison between the results obtained through the proposed approach and those of a traditional HRA method such as human error assessment and reduction technique, shows the capability of the simulator to provide coherent and accurate analysis. r

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Modelling and Simulation of Human Behaviour in System Control

Cited by 76 publications

References 0 publications

Stabilization of unknown nonlinear systems using a cognition‐based framework

Stabilization of unknown nonlinear systems using a cognition‐based framework

Dynamically deployed support as a potential solution to negative behavioral adaptation

A probabilistic cognitive simulator for HRA studies (PROCOS)

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