Model analysis of adaptive car driving behavior

This paper deals with two modeling approaches to car driving. The first one is a system theoretic approach to describe adaptive human driving behavior. The second approach utilizes neural networks. As an illustrative example the overtaking task is considered and modeled in system theoretic terms. Model results are used to teach a neural network. The results show that a neural network is able to learn this task even when certain task variables change. The next step is to perform an experiment with real human operators in order to assess the validity of both modeling approaches and their relative meri

Modeling human learning involved in car driving

In this paper, car driving is considered at the level of human tracking and maneuvering in the context of other traffic. A model analysis revealed the most salient features determining driving performance and safety. Learning car driving is modelled based on a system theoretical approach and based on a neural network approach. The aim of this research is to assess the relative merit of both approaches to describe human learning behavior in car driving specifically and in operating dynamic systems in general

A model of the vessel traffic process

A model of the total vessel traffic control process that includes the functioning of the human operator (HO) is presented. The vessel traffic services (VTSs) are modeled in their possible role of monitor, conflict detector, and advisor for the total vessel traffic system. The model assumes a number of ships, with a given planned route, in a given confined area. The navigation of each ship is based on a planned route, which is updated by information about the visual scene, instruments, and the VTS. Both normal operation and collision avoidance are modeled. The model is implemented in a C program. Typical traffic situations have been simulated to showing the ability of the model to address realistic vessel traffic scenarios. The model can answer questions related to safety and efficiency, the effect of HO functioning, information necessary to perform tasks, communication between ships and VTS, the optimization of procedures, automation of the total vessel traffic process, et

Model of large scale man-machine systems with an application to vessel traffic control

Mathematical models are discussed to deal with complex large-scale man-machine systems such as vessel (air, road) traffic and process control systems. Only interrelationships between subsystems are assumed. Each subsystem is controlled by a corresponding human operator (HO). Because of the interaction between subsystems, the HO has to estimate the state of all relevant subsystems and the relationships between them, based on which he can decide and react. This nonlinear filter problem is solved by means of both a linearized Kalman filter and an extended Kalman filter (in case state references are unknown and have to be estimated). The general model structure is applied to the concrete problem of vessel traffic control. In addition to the control of each ship, this involves collision avoidance between ship

Modeling human operator involvement in robotic systems

A modeling approach is presented to describe complex manned robotic systems. The robotic system is modeled as a (highly) nonlinear, possibly time-varying dynamic system including any time delays in terms of optimal estimation, control and decision theory. The role of the human operator(s) is modeled varying from supervisor of the automated part of the system to controller in terms of the various functions involved to perform goal-oriented tasks. It can be expected that the model is capable of answering questions related to reliability and efficiency, design alternatives, function allocation, automation, et