Modelling Human-Driver BehaviourUsing a Biofidelic Approach

García, Miguel Martínez (2018) Modelling Human-Driver BehaviourUsing a Biofidelic Approach. PhD thesis, University of Lincoln.

Modelling Human-Driver BehaviourUsing a Biofidelic Approach
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Martínez Miguel - Engineering - December 2018.pdf - Whole Document

Item Type:Thesis (PhD)
Item Status:Live Archive


This dissertation is concerned with the subject of modelling human steering control of groundvehicles. Special care has been taken with respect to designing a model that isbiofidelic, i.e.,a model that operates according to theprinciples of human control.With this aim, first classical human control theory has been revisited, both from aliterature review and an experimental perspective; data have been recorded from test subjectsin compensatory and pursuit tracking tasks. The tracking experiments are the first ever to beperformed with fractional order plants, which are plants suitable to represent system memory.From the data, an extension of the Crossover model by McRuer’s is designed, to includethe control of such category of plants. The proposed model is referred to as theFractionalCrossover Model. This is followed by a study on modelling memory in human-machinesystems from a classical control theory viewpoint. These results broaden the existing array ofmanual control modelling techniques and can be employed in a modular manner, combinedwith current models.More significantly – and still with respect to the domain of generic human control andhuman-machine systems – a new approach for modelling the human-operator is proposed.This approach consists in treating the problem from a statistical viewpoint. With thismethodology a novel human control model based on multiplicative dynamics is presented.The model, which was inspired on actual results in neuroscience, is validated with thetracking data obtained from test subjects and by comparing it to classical models in theliterature. Hence the model is useful to analyse human performance or to reproduce humancontrol in simulation, field tests or in the video game industry.With respect to steering control modelling, which is the main topic of this dissertation,additional experiments with test subjects were conducted in a simple vehicle simulator – withhardware and software specifically developed during this research program to test multiplehypotheses. The data were analysed with the intent of identifying which optical variablesdrivers employ while controlling a vehicle on public roads; it is seen that the splay angles– which are the projections of the road lines on the retina – are likely candidates for lanekeeping at low speeds. This brings on a novel human-centred driver model first proposed
xhere. This model includes multiplicative human control over the splay angles, and far-pointerror perception for lane keeping at higher speeds.The human-centred model has its domain of applicability in the intelligent transportationindustry, in particular for the development of shared control systems and advanced driver-assistance systems for semi-autonomous ground vehicles. Additionally, the model can beemployed in field testing of ground vehicles – for example, in vehicle durability tests.Furthermore, the topic of alternative steering devices for driving autonomous and semi-autonomous vehicles is investigated. This leads to another of the contributions in thisdissertation. Here it is proposed that for such vehicles, and for the control of systems with ashared controlperspective, anisometric steering wheelcan be advantageous under certainschemes – tight rein or loose rein modes according to the H-metaphor. This is supportedby additional data collected in the driving simulation experiments. Resulting from this,fractional order transfer functions are employed to increment steering stability and controlaccuracy with the isometric device. This prototypical steering system is applicable for thecontrol of ground vehicles with the so-calledby-wirecontrols, which are already incorporatedin some commercially available vehicles.

Divisions:College of Science > School of Engineering
ID Code:37686
Deposited On:07 Oct 2019 11:40

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