A review of human sensory dynamics for application to models of driver steering and speed control

Nash, Christopher; Cole, David J.; Bigler, Robert S.

doi:10.1007/s00422-016-0682-x

Cited by 68 publications

(67 citation statements)

References 197 publications

(274 reference statements)

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“…As explained in Section 1, previous studies reviewed in [13] have shown that measurements of sensory perception taken in passive conditions may not be applicable to active control tasks such as driving [7][8][9][10][11]. Therefore, most of the parameters of the model are found using an identification procedure to fit to experimental results.…”

Section: Model Transfer Functions and Parametersmentioning

confidence: 99%

“…An active control task such as driving requires attention to be shared between the task itself and the perception of concurrent sensory stimuli, in contrast with passive perception tasks where the subject is concentrating solely on one sensory stimulus. Nash and Cole [12], building upon the work of Bigler [6], and upon a review of the literature [13], developed an improved driver model incorporating sensory dynamics. Preliminary analysis of this model was carried out [14] using published results from an experiment in a flight simulator [15] to validate the modelling approach for an aeroplane control task.…”

Section: Introductionmentioning

confidence: 99%

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Identification and validation of a driver steering control model incorporating human sensory dynamics

Nash

Cole

2019

Vehicle System Dynamics

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Most existing models of driver steering control do not consider the driver's sensory dynamics, despite many aspects of human sensory perception having been researched extensively. The authors recently reported the development of a driver model that incorporates sensory transfer functions, noise and delays. The present paper reports the experimental identification and validation of this model. An experiment was carried out with five test subjects in a driving simulator, aiming to replicate a real-world driving scenario with no motion scaling. The results of this experiment are used to identify parameter values for the driver model, and the model is found to describe the results of the experiment well. Predicted steering angles match the linear component of measured results with an average 'variance accounted for' of 98% using separate parameter sets for each trial, and 93% with a single fixed parameter set. The identified parameter values are compared with results from the literature and are found to be physically plausible, supporting the hypothesis that driver steering control can be predicted using models of human perception and control mechanisms. ARTICLE HISTORY

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Section: Model Transfer Functions and Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification and validation of a driver steering control model incorporating human sensory dynamics

Nash

Cole

2019

Vehicle System Dynamics

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“…As for the vestibular noise, the literature on how visual and vestibular sensory systems compare in terms of delays or noise levels provide conflicting information for different types of experimental condition Nash et al (2016), so for simplicity we here assume σ vis = σ ves , to begin with. However, later in this paper we also examine the model's sensitivity to variations in these noise levels.…”

Section: Visual-vestibular Integrationmentioning

confidence: 99%

Modelling visual-vestibular integration and behavioural adaptation in the driving simulator

Markkula

Romano

Waldram

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

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It is well established that not only vision but also other sensory modalities affect drivers' control of their vehicles, and that drivers adapt over time to persistent changes in sensory cues (for example in driving simulators), but the mechanisms underlying these behavioural phenomena are poorly understood. Here, we consider the existing literature on how driver steering in slalom tasks is affected by the down-scaling of vestibular cues, and propose a driver model that can explain the empirically observed effects, namely: decreased task performance and increased steering effort during initial exposure, followed by a partial reversal of these effects as task exposure is prolonged. Unexpectedly, the model also reproduced another empirical finding: a local optimum for motion down-scaling, where path-tracking is better than when one-to-one motion cues are available. Overall, the results imply that: (1) drivers make direct use of vestibular information as part of determining appropriate steering, and (2) motion down-scaling causes a yaw rate underestimation phenomenon, where drivers behave as if the simulated vehicle is rotating more slowly than it is. However, (3) in the slalom task, a certain degree of such yaw rate underestimation is beneficial to path tracking performance. Furthermore, (4) behavioural adaptation, as empirically observed in slalom tasks, may occur due to (a) down-weighting of vestibular cues, and/or (b) increased sensitivity to control errors, in determining when to adjust steering and by how much, but (c) seemingly not in the form of a full compensatory rescaling of the received vestibular input. The analyses presented here provide new insights and hypotheses about simulator driving, and the developed models can be used to support research on multisensory integration and behavioural adaptation in both driving and other task domains.

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“…Previous studies have shown that passive tasks are quite different from active tasks in terms of brain activities and perception of sensory information. Flach (1990) addressed the difference on control models between being an actor and being an observer, see also (Nash, Cole et al 2016). Through fMRI studies, Walter, Vetter et al (2001) detected numerous differences between active and passive driving (i.e.…”

Section: Visual Cues For Steeringmentioning

confidence: 99%

“…Open-loop "trajectory planning" does not need to be absolutely correct to be effective. Nash, Cole et al (2016) describe a steering model in which steering was largely controlled in a feed-forward manner. Although errors were generated by the model (due to an imperfect internal model, sensory error/delay, and/or other sources of noise) the model appeared to capture real-world human behaviour.…”

Section: Internal Model Of Steeringmentioning

confidence: 99%

Sensorimotor basis of motor vehicle control

Xu¹

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Driving represents one of the most common modes of transport world-wide. It is also one of the major causes of death and injury in developed societies prompting technological innovations through which computers share or assume control of the task of driving, through a range of Advanced Driver Assisting Systems (ADAS). Given the lengthy and substantial investment in such systems it is perhaps surprising how little we understand about how humans control motor vehicles and why, on occasion, this control fails. This is actually a problem. Driver-assist systems that do not understand human drivers well can be dangerous and promote unforeseen risks. For example, anti-lock braking systems, which are designed to prevent skidding during heavy braking, do not always have the positive impact on safety one might expect (Farmer, Lund et al. 1997, Sagberg, Fosser et al. 1997, perhaps because their operation does not mesh seamlessly with the expectations of drivers or other road users.One route to gaining a better understanding of the control processes employed by drivers, is through the study of situations in which standard steering strategies fail. In this thesis the In this thesis, we investigated this effect in a series of experiments aimed at getting to the bottom of the causes of this error. The thesis begins by seeking to generalize the effect from the special case of a straight road which was the focus of earlier studies. This was deemed important because a typical steering wheel has a natural tendency to re-centre itself. It is possible that this behaviour reduces the active steering movements a driver must make during a lane change, since they do not have to actively return the steering-wheel to the neutral position. For that reason, we designed a circular road on which a non-zero steering wheel angle was required at all times. Through this study, we were able to conclude that the effect does generalize.In a second set of experiments we attempted to investigate which visual cues are essential for drivers to correct their error. Apparently, a normal road with redundant information provides sufficient visual feedback for drivers to make a lane change. However, it is of interest to know to what extent the visual feedback can be reduced and still meet the minimum requirement. In our study, we chose optic flow as a starting point. Optic flow has previously been shown to suffice for the purpose of heading perception and heading control.A number of 'steering-towards-a-target' studies also found that optic flow is tightly related to ii steering performance. Hence, we asked whether optic flow was sufficient for controlling a lane change manoeuvre and, unlike much of the previous studies on flow, we posed the question in an active steering task, revealing that flow alone is not sufficient to prompt correct lane changing behaviour.In the concluding set of experiments we took our studies out into the field. Most previous studies on lane changing have been conducted in driving simulators. Simulators are limited in that th...

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A review of human sensory dynamics for application to models of driver steering and speed control

Cited by 68 publications

References 197 publications

Identification and validation of a driver steering control model incorporating human sensory dynamics

Identification and validation of a driver steering control model incorporating human sensory dynamics

Modelling visual-vestibular integration and behavioural adaptation in the driving simulator

Sensorimotor basis of motor vehicle control

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