Research Papers

Measurement and Modeling of the Effect of Sensory Conflicts on Driver Steering Control

[+] Author and Article Information
Christopher J. Nash

Department of Engineering,
University of Cambridge,
Cambridge CB2 1PZ, UK

David J. Cole

Department of Engineering,
University of Cambridge,
Cambridge CB2 1PZ, UK
e-mail: djc13@cam.ac.uk

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received August 17, 2017; final manuscript received February 4, 2019; published online March 13, 2019. Assoc. Editor: Tesheng Hsiao.

J. Dyn. Sys., Meas., Control 141(6), 061012 (Mar 13, 2019) (11 pages) Paper No: DS-17-1412; doi: 10.1115/1.4042876 History: Received August 17, 2017; Revised February 04, 2019

In previous work, a new model of driver steering control incorporating sensory dynamics was derived and used to explain the performance of drivers in a simulator with full-scale motion feedback. This paper describes further experiments investigating how drivers steer with conflicts between their visual and vestibular measurements, caused by scaling or filtering the physical motion of the simulator relative to the virtual environment. The predictions of several variations of the new driver model are compared with the measurements to understand how drivers perceive sensory conflicts. Drivers are found to adapt well in general, unless the conflict is large, in which case they ignore the physical motion and rely on visual measurements. Drivers make greater use of physical motion which they rate as being more helpful, achieving a better tracking performance. Sensory measurement noise is shown to be signal-dependent, allowing a single set of parameters to be found to fit the results of all the trials. The model fits measured linear steering behavior with an average “variance accounted for (VAF)” of 86%.

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Fig. 1

Structure of the driver model, reproduced from Ref. [2]

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Fig. 2

Structure of plant in the driver model (adapted from Ref.[2])

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Fig. 3

Model of the driver's visual system

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Fig. 4

Visual display of target line to drivers, with and without preview. Note that the display used in the experiments was much more realistic than these illustrative images.

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Fig. 5

Bode diagram of motion filters HHP1(s) and HHP2(s)

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Fig. 6

VAF values found for each model variation using the results of the trials with scaled motion, without preview

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Fig. 7

Identified measurement noise amplitudes versus RMS signal amplitudes, using model M2. RMS values correspond to perceived signals, filtered by sensory transfer functions. Vestibular noise amplitudes Va and Vω are not plotted for trials with no translational or rotational motion. Visual noise amplitudes Ve and σϕ are only plotted for the three no-motion trials. Trend lines ignore the high values at low amplitudes for Va and Ve.

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Fig. 8

Variance accounted for using a single parameter set identified to fit all trials, with scaled motion and without preview

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Fig. 9

Variance accounted for values found for each model variation using the results of the trials with scaled motion, with preview

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Fig. 10

Correlation between metrics for scaled motion experiment with preview: RMS path-following error; difference in VAF values between models M2 and M0; and average driver subjective ratings

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Fig. 11

Variance accounted for values found for each model variation using the results of the trials with filtered motion

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Fig. 12

Variance accounted for values found for each model variation using the results of the trials with full motion

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Fig. 13

Variance accounted for values for all trials using a single set of parameter values, compared with VAFs for parameters found individually for each trial and the Box–Jenkins upper bound

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Fig. 14

Bounds for ratio of measured to predicted noise amplitude. Predicted noise amplitude is defined by the identified single set of parameter values, measured noise amplitude is defined as RMS (δsimδexp) for the upper bound and RMS (δBJδexp) for the lower bound.



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