Gastcollege HAN Master of Control Systems Engineering
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Transcript of 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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Assessment Methods to judge(Driver-)Vehicle Handling (2)
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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Assessment Methods to judge(Driver-)Vehicle Handling (3)
= 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 90%
Cornering stiffness 100% (reference)
Cornering stiffness 110%
Cornering stiffness 120%
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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 80%
Cornering stiffness 90%
Cornering stiffness 100% (reference)
Cornering stiffness 110%
Cornering stiffness 120%
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Results non adaptive driver
0.0440.66
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