Gastcollege HAN Master of Control Systems Engineering

8/14/2019 Gastcollege HAN Master of Control Systems Engineering

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Driver Models for Tyre Testing:Why and How?

Master Control Systems Engineering

27 May 2009

Ir. Saskia Monsma


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Overview

Introduction

Research project Driver modelling

Simulation study

Experiments

Conclusions & Follow Up


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Introduction

Researcher at Mobility Technology research &

lecturer for Automotive engineering PhD-research:

How to improve assessment methodsto judge driver-vehicle handling

in relationship with tyre characteristics?


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Handling, tyre characteristics

Handling: cornering behaviour+ the drivers perception

Tyre characteristics

Tyrecharacteristics

Construction compoundply-type

carcass

belt

Dimensionaspect ratio

size

Servicetemperature

wet/dryconditions

Inner pressure

Performance

aligning torque

cornering stiffness

pneumatic trail

peak lateral forcecoefficient

braking force

coefficient

Aging

wear-in

wear after normal use

slip angle

V

Fy

0 5 10 15 (deg)


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Relation:Tyre Characteristics

Driver-Vehicle Handlingis not straightforward

Many different tyre parameters

There is a lot between tyre characteristics andvehicle performance

steerbywire

(active

)suspe

nsion

electronicstabilitycontrol

anti-lockbrakingsy

stem

tractioncon

trol

advanced

driverassist

system


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Relation:Tyre Characteristics

Driver-Vehicle Handlingis not straightforward

Many different tyre parameters

There is a lot between tyre characteristics andvehicle handling

Vehicle handling performance needs to betranslated into tyre characteristics

What is good driver-vehicle handling?

Subjective (depends on person, brand of vehicle, etc. )

Depends on drivers mental workload and control effort

How to judge driver-vehicle handling? different assessment methods


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Assessment Methods to judge(Driver-)Vehicle Handling (1)

Objective vehicle tests Driver = steering machine characteristic data (e.g., response times, overshoot,

bandwidth,..)

Subjective rating Controllability, steerability, etc.

Questions, statements: agree/disagree

Closed loop achievement

Driver must perform task as best as he can

Circuit, (double) lane change on max. speed, elk-test, slalomon max. speed, etc.

Reallifetesting


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Workload measures Driver performs a certain task (manoeuvre, sec. task)

Steering Reversal Rate, High Frequency Area, Time to LineCrossing

Combined primary and secondary task performance Driver performs primary and secondary task (improve task)

Performance on primary and/or secondary task

Restriction of driver input

limited vision (glasses), driver decides for opening/closing task performance and frequency of opening/closing

Physiological output Muscle tension, blood pressure, heart rate variability

Reallifetesting


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= Simulating vehicle behaviour according tothe procedures as prescribed in testprotocols

open loop: vehicle + tyres

closed loop: vehicle + tyres + driver

Advantage: optimisation of vehicle + tyresbehaviour beforethe vehicle is built

Used by vehicle manufacturers and byautomotive suppliers

Virtualtes

ting

drivermodels


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Driver Modelling

In objective tests: driver = steering machine

In subjective test: driver = black box Driver model for opening the black box

Analysis gives further understanding of the relation:Tyre Characteristics Driver-Vehicle Handling


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Research Topics

1. Driver models (professional test driver)

2. Drivers mental workload and control effortmeasures

3. Neural networks for the assessment of driverjudgement and control of vehicle performance

4. Design of assessment tools(based on and refining research topics 1-3)


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Human behaviour and driving tasks

There are many different driver models for differentdriver behaviour Provide insights into basic properties of human performance Predict the performance of the driver-vehicle system

(stability) Driver assistance systems

SRK-model for human behaviour(Rasmussen)


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DARPA Urban Challenge

Vehicles with no driverand no remote control

60 miles urban area

course with traffic Obeying all traffic

regulations


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Model the Driver

requiredtrajectory

roadconditions

driver

steeringcontrol

throttlebrake

vehicle

road air

deviation from path, in orientation,following time, distance,..

vibrations, noise,

disturbances

modelled with

linear differential equations

also?


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Model the Human Controller

Describing functions (= approximate

transfer functions) of human performanceusing control language

Can you model human performance bylinear models? Thresholds Detect and remember patterns

Learn and adapt

Yes, with a quasi-linear model and with Stationary tracking task by highly trained

controllers

Unpredictable input

non-linear


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Quasi-Linear Model of the Human Controller

YH = linear transfer function

u(t) = linear response

n(t) = internal noise (perceptual and motor system,uncorrelated with input signal)

u(t) = quasi linear response


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Adaptive Nature of the Driver

Drivers can adapt to changing vehicle

behaviour although vehicle behaviour changes,

overall driver-vehicle performance canremain the same

Drivers can sense small differencesin handling behaviour


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Relation with Mental Workload

Primary task performance measures will only be sensitive inregions D and B, not in A1, A2, A3. Most self report measuresare sensitive in all but A2

boredom, loss ofsituation awareness

and reduced alertness

overloaded


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YH(j)

McRuer Crossover Model

limitations of the human

reaction time

neuromuscularlag

adjusted toachieve goodcontrol

gain

lead

lag

YH


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Simulation study

Will the driver adapt his parameters for

different tyres? Path tracking

path


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Simulation study models


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Optimisation of driver controller gains

Based on minimisation of cost function:

J =(current path error)2+ weight *(steer workload)2

Parameters: Preview time = 1.5s

Weight = 1

V = 25m/s Path:

0 100 200 300 400 500 600 700 800 9000

100

200

x

y

= steer speed

=d(steer angle)/dt


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Different tyre characteristics:

cornering stiffness


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Simulation with two virtual drivers

Driver controller gains are optimised

(based on cost function) for reference tyrecharacteristic (= reference driver gains)

Simulations with different tyrecharacteristics for two virtual drivers

non adaptive driver (with reference drivergains: )

adaptive driver (with - for each different tyrecharacteristic - optimised driver gains)


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Errors non adaptive driver

0 5 10 15 20 25 30 35 40 45-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8lateral current error versus time

time(s)

lateralcurrenterror(m)

0 5 10 15 20 25 30 35 40 45

-10

-5

0

5

10

steer speed versus time

time(s)

steerspeed(deg/s)

Cornering stiffness 80%


Cornering stiffness 100% (reference)




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Errors adaptive driver

0 5 10 15 20 25 30 35 40 45-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8lateral current error versus time

time(s)

lateralcurrenterror(m)

0 5 10 15 20 25 30 35 40 45

-10

-5

0

5

10

steer speed versus time

time(s)

steerspeed(deg/s)



Cornering stiffness 100% (reference)




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Results non adaptive driver

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Cost function for different tyre characteristics

0%

50%

100%

150%

200%

250%

300%

350%

80% 90% 100% 110% 120%Cornering stiffness

J

sqr(current path error)

weight*sqr(steer workload)

Human controller gains versus

different tyre characterisitics

60%

70%

80%

90%

100%

110%

120%

130%

140%

80% 90% 100% 110% 120%

Cornering stiffness

Preview path errorgain (%)

Preview orientation

error gain (%)


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Results adaptive driver

Human controller gains versus

different tyre characterisitics

60%

70%

80%

90%

100%

110%

120%

130%

140%

80% 90% 100% 110% 120%

Cornering stiffness

Preview path error

gain (%)Preview orientation

error ain %

Cost function for different tyre characteristics

0%

50%

100%

150%

200%

250%

300%

350%

80% 90% 100% 110% 120%

Cornering stiffness

J

sqr(current path error)

weight*sqr(steer workload)

0.0440.66


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Objectives experiments

More Understanding on Subjective Evaluation

1. Correlation between objective criteria andsubjective evaluation

2. Experimental derived workload measures(control effort, mental workload)

3. Evaluation of driver model parametersaccounting for subjective evaluation

Also

New test vehicle Testing of driver measurements


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Experiments

Same tests are performed with different

tyres keeping driver, vehicle and environment as

constant as possible differences relatedto the tyres

keeping tyres, vehicle and environment asconstant as possible differences relatedto the driver


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Experiments: Set Up

Test vehicle + measurements

Vehicle dynamics (x,y,z: velocities,accelerations, angles, angl.vel.,)

Steering wheel (steering angle,steering angle velocity, moment)

Two professional tyre test drivers Driver measurements

Cameras

Heart beat


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Test Track: Test Centre Lelystad


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Experiments: Tyres

Choice based

on expectedhandling behaviour

Measured

slip angle []

Lateralforce[N]

winter all season summer


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Experiments: Content

Objective tests (ISO-standards):

steady state circle, step steer, puls steer (10-20 repetitions of each driver-tyre

combination)

Subjective evaluation

Mini circuiton highest possible speed

blind testing in badges:

1,2,3 / 2,3,4 / 5,6 9 evaluation aspects

+ overall judgement


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Subjective evaluation aspects

Steering precision while cornering

Stability while cornering (no throttle change)

Stability while cornering (throttle change)

Yaw overshoot

Predictability

Yaw delay Steering angle

Grip

Controllability Overall judgment

Comment

T t k i i


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Test week impression


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Results Overall Judgement


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Influence Tyres on Evaluation Aspects

Yaw delay

+ Steering precision Stability while

cornering (no throttle

change) Grip

Steering angle


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Correlation Objective Measurements

with Subjective Evaluation

Step steer response time for lateralacceleration

(time delay between 50%steering angle and 90%steady state value)


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Correlation Objective Measurements

with Subjective Evaluation

Step steer response time for lateralacceleration


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Results puls steer: bandwidth yaw rate

tyre in non linear range?


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Workload Measure: High Frequency Area

Indicator for workload: High Frequency Areaarea beneath curve fcut-flimit

area beneath curve 0-fcutHFA =


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Results High Frequency Area


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Optimisation of

Driver Model Parameters Ld and Kd

Cost functional for optimising driver modelparameters Ld and Kd for the different tyres

Small variation in Ld and Kdin contrast to non-extreme conditions!(Monsma: Tyre Technology Int., Annual Review, 2008)

( ) ( ) dtwdtFC

...2

2

+=

path error weight factorsteering rate

tracking performance workload


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Conclusions & Follow Up

HFA as workload measurement is promising forcorrelation with subjective evaluation

Investigation of mental workload for extrememanoeuvring (heart rate measurements, video)

Driver model parameter adjustment is limited in

extreme manoeuvring conditions in contrast tonon-extreme conditions.

Explore driver parameter adjustment forrelation:

nonextreme conditions subjective evaluation

Workload measurements (and modelling)


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Videos test drivers

Documents

Gastcollege HAN Master of Control Systems Engineering