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TECHNICAL PAPERS

A Stochastic Form of a Human Driver Steering Dynamics Model

[+] Author and Article Information
Magomed Gabibulayev

Department of Mechanical and Aeronautical Engineering, University of California—Davis, Davis, CA 95616gabibulayev@ucdavis.edu

Bahram Ravani

Department of Mechanical and Aeronautical Engineering, University of California—Davis, Davis, CA 95616bravani@ucdavis.edu

Spectral densities are used for determining variances of signals of stationary systems only; two-dimensional autocovariance functions have to be used for analysis of nonstationary systems.

The Laplace transformation works with limited classes of time-varying differential equations, having, for example, only polynomial or exponential coefficients; equations after transformation are not algebraic as in case of the Laplace transformation of linear time invariant differential equations; therefore, this transformation is rarely used for time-varying systems.

J. Dyn. Sys., Meas., Control 129(3), 322-336 (Feb 03, 2005) (15 pages) doi:10.1115/1.2098927 History: Received February 02, 2004; Revised February 03, 2005

This work develops a stochastic form of a human driver model which can be used for simulating vehicle guidance and control. The human motor-control function is complex and can be affected by factors such as driver’s training and experience, fatigue, road conditions, and attention. The variations in these effects become more pronounced in hazardous driving conditions such as in snow and ice. One example of such driving conditions is snow removal operation in highway maintenance, where the use of a stochastic driver model seems to be more desirable. This work evaluates and extends existing models of a human driver including stochastic or statistical considerations related to differences in drivers’ experiences and their conditions as well as variations in the effect of disturbances such as plowing forces. The aim is to develop a simulation environment that can be used in design and evaluation of driver assistance systems for snow removal operation in an Intelligent Transportation System environment.

Copyright © 2007 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

McRuer’s compensatory/anticipatory/precognitive driver model

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Figure 2

Hess and Modjtahedzadeh’s driver model

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Figure 3

MacAdam’s optimal driver model

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Figure 4

Ukawa’s driver model

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Figure 5

Top view of snowplowing operation

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Figure 6

Control system for lateral displacement of the snowplow with Hess-Modjtahedzadeh’s driver model

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Figure 7

Equivalent block diagram representation of a stochastic gain Ky

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Figure 8

A block diagram representation of a product nonlinearity with n inputs

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Figure 9

A block diagram representation of a statistically linearized product nonlinearity with n inputs

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Figure 10

Equivalent block diagram for a combination of 26,28

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Figure 11

Equivalent block diagram for a combination of 29,30

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Figure 12

A block diagram for expected values of signals

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Figure 13

Equivalent block diagram for expected values of signals with respect to spectral characteristics

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Figure 14

A block diagram for centered values of signals

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Figure 15

Equivalent block diagram for centered values of signals with respect to spectral characteristics

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Figure 16

Two degree-of-freedom vehicle model including plow blade

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Figure 17

Lane change maneuver with only one stochastic gain Ky (plow blade off the ground)

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Figure 18

Lane change maneuver with two stochastic parameters: Ky and T3 (plow blade off the ground)

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Figure 19

Lane change maneuver with three stochastic parameters: Ky, T3, and T4 (plow blade off the ground)

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Figure 20

Lane change maneuver with stochastic parameters Ky, T3, and T4 (plow blade on the ground)

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Figure 21

Steering wheel angle: expected values mδ(t); upper boundary δU; lower boundary δL (plow blade off the ground)

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Figure 22

Steering wheel angle: expected values mδ(t); upper boundary δU; lower boundary δL (plow blade on the ground)

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Figure 23

Lane change maneuver with stochastic parameters Ky, T3, and T4 (plow blade off the ground), time-varying expected values and variances of these parameters

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Figure 24

Lane change maneuver with stochastic parameters Ky, T3, and T4 (plow blade on the ground) and time-varying expected values and variances of these parameters

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Figure 25

Steering wheel angle: expected values mδ(t); upper boundary δU; lower boundary δL (plow blade off the ground)

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Figure 26

Steering wheel angle: expected values mδ(t); upper boundary δU; lower boundary δL (plow blade on the ground)

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Figure 27

Experimental data on the lateral displacement of the vehicle: mYVEXP(t)—expected values; YVEXPU—upper boundary; YVEXPL—lower boundary

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Figure 28

Model results on the lateral displacement of the vehicle: mYVMOD(t)—expected values; YVMODU—upper boundary; YVMODL—lower boundary

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Figure 29

Model results and the experimental data

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