Driver Models for Tyre Testing:
Why and How?
Master Control Systems Engineering
27 May 2009
Ir. Saskia Monsma

Overview
Introduction
Research project
Driver modelling
Simulation study
Experiments
Conclusions & Follow Up

Introduction
Researcher at Mobility Technology research &
lecturer for Automotive engineering
PhD-research:
How to improve assessment methods
to judge driver-vehicle handling
in relationship with tyre characteristics?

Handling, tyre characteristics
Handling: cornering behaviour
+ the driver’s perception
Tyre characteristics
Fy
slip angle α
cornering stiffness V
aligning torque
pneumatic trail Inner pressure
peak lateral force Performance
temperature
coefficient Service
wet/dry
braking force conditions
coefficient
Tyre
size characteristics carcass
Dimension ply-type
aspect ratio
Construction compound
belt
Aging
wear after normal use
wear-in
0 5 10 15 (deg)

Relation:Tyre Characteristics Driver-
Vehicle Handling is not straightforward
Many different tyre parameters
There is a lot between tyre characteristics and
sion
vehicle performance…
n
spe
) su
tive
(ac
tion control ele
trac ctr
on
ic
ad sta
dr vanc steer b
y wire b il
i ty
sy ive ed
ste r as co
nt r
m sis ol
t m
g syste
k brakin
an ti-loc

Relation:Tyre Characteristics Driver-
Vehicle Handling is not straightforward
Many different tyre parameters
There is a lot between tyre characteristics and
vehicle handling…
Vehicle handling performance needs to be
‘translated’ 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

Assessment Methods to judge
(Driver-)Vehicle Handling (1)
eal life testing
R
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, slalom
on max. speed, etc.

Assessment Methods to judge
(Driver-)Vehicle Handling (2)
eal life testing
R
Workload measures
– Driver performs a certain task (manoeuvre, sec. task)
– Steering Reversal Rate, High Frequency Area, Time to Line
Crossing
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

Assessment Methods to judge
(Driver-)Vehicle Handling (3)
Virtual testing
= Simulating vehicle behaviour according to
the procedures as prescribed in test
protocols driver
models
– open loop: vehicle + tyres
– closed loop: vehicle + tyres + driver
Advantage: optimisation of vehicle + tyres
behaviour before the vehicle is built
Used by vehicle manufacturers and by
automotive suppliers

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

Research Topics
1. Driver models (professional test driver)
2. Drivers mental workload and control effort
measures
3. Neural networks for the assessment of driver
judgement and control of vehicle performance
4. Design of assessment tools
(based on and refining research topics 1-3)

Driver-Vehicle System Model
perception
action disturbances
road air
steering
road control
conditions vehicle
driver
required throttle
trajectory brake
vibrations, noise,…
deviation from path, in orientation,
following time, distance,..
Open-loop system
Closed-loop system

Human behaviour and driving tasks
SRK-model for human behaviour
(Rasmussen)
There are many different driver models for different
driver behaviour
– Provide insights into basic properties of human performance
– Predict the performance of the driver-vehicle system
(stability)
– Driver assistance systems

DARPA Urban Challenge
Vehicles with no driver
and no remote control
60 miles urban area
course with traffic
Obeying all traffic
regulations

Model the Driver
disturbances
road air
steering
road control
conditions vehicle
driver
required throttle
trajectory brake
vibrations, noise,…
also?
deviation from path, in orientation,
following time, distance,..
modelled with
linear differential equations

Model the Human Controller
Describing functions (= approximate
transfer functions) of human performance
using “control language”
Can you model human performance by
linear models? non-linear
– 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

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

Adaptive Nature of the Driver
Drivers can adapt to changing vehicle
behaviour
– although vehicle behaviour changes,
overall driver-vehicle performance can
remain the same
Drivers can sense small differences
in handling behaviour

Relation with Mental Workload
boredom, loss of
situation awareness overloaded
and reduced alertness
Primary task performance measures will only be sensitive in
regions D and B, not in A1, A2, A3. Most self report measures
are sensitive in all but A2

McRuer Crossover Model
YH
limitations of the human
gain
reaction time
adjusted to
lead achieve good
control
YH(jω) lag
neuromuscular
lag

Simulation study
Will the driver adapt his parameters for
different tyres?
Path tracking
th
pa

Simulation study models

Optimisation of driver controller gains
Based on minimisation of cost function:
J = ∫(current path error)2 + weight * ∫(steer workload)2
= steer speed
=d(steer angle)/dt
Parameters:
– Preview time = 1.5s
– Weight = 1 Current defined path
200
– V = 25m/s
– Path:
y
100
0
0 100 200 300 400 500 600 700 800 900
x

Different tyre characteristics:
cornering stiffness

Simulation with two virtual drivers
Driver controller gains are optimised
(based on cost function) for reference tyre
characteristic (= reference driver gains)
Simulations with different tyre
characteristics for two virtual drivers
– non adaptive driver (with reference driver
gains: )
– adaptive driver (with - for each different tyre
characteristic - optimised driver gains)

Errors non adaptive driver
lateral current error versus time steer speed versus time
0.8
10
0.6
5
0.4
steer speed(deg/s)
lateral current error (m)
0.2
0
0
-5
-0.2
-10
-0.4
0 5 10 15 20 25 30 35 40 45
Cornering time(s) 80%
stiffness
-0.6 Cornering stiffness 90%
Cornering stiffness 100% (reference)
Cornering stiffness 110%
Cornering stiffness 120%
-0.8
0 5 10 15 20 25 30 35 40 45
time(s)

Errors adaptive driver
lateral current error versus time steer speed versus time
0.8
10
0.6
5
0.4
steer speed(deg/s)
lateral current error (m)
0.2
0
0
-5
-0.2
-10
-0.4
0 5 10 15 20 25 30 35 40 45
Cornering time(s) 80%
stiffness
-0.6 Cornering stiffness 90%
Cornering stiffness 100% (reference)
Cornering stiffness 110%
Cornering stiffness 120%
-0.8
0 5 10 15 20 25 30 35 40 45
time(s)

Results non adaptive driver
Human controller gains versus
different tyre characterisitics Cost function for different tyre characteristics
140% Preview path error 350%
sqr(current path error)
gain (%)
130% weight*sqr(steer workload)
Preview orientation 300%
error gain (%)
120%
250%
110%
200%
0.044 100%
0.66
J
150%
90%
80% 100%
70% 50%
60%
0%
80% 90% 100% 110% 120% 80% 90% 100% 110% 120%
Cornering stiffness Cornering stiffness

Results adaptive driver
Human controller gains versus
different tyre characterisitics Cost function for different tyre characteristics
Preview path error
140% 350%
gain (%) sqr(current path error)
Preview orientation
130% error gain (%) weight*sqr(steer workload)
300%
120%
250%
110%
0.044 100%
200%
0.66
J
90% 150%
80% 100%
70%
50%
60%
80% 90% 100% 110% 120% 0%
Cornering stiffness 80% 90% 100% 110% 120%
Cornering stiffness

Objectives experiments
More Understanding on Subjective Evaluation
1. Correlation between objective criteria and
subjective evaluation
2. Experimental derived workload measures
(control effort, mental workload)
3. Evaluation of driver model parameters
accounting for subjective evaluation
Also
– New test vehicle
– Testing of driver measurements

Experiments
Same tests are performed with different
tyres
– keeping driver, vehicle and environment as
constant as possible differences related
to the tyres
– keeping tyres, vehicle and environment as
constant as possible differences related
to the driver

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
– Camera’s
– Heart beat

Test Track: Test Centre Lelystad

Experiments: Tyres
Choice based
on expected
winter all season summer
handling behaviour
Measured
Lateral force [N]
slip angle [°]

Experiments: Content
Objective tests (ISO-standards):
steady state circle, step steer, puls steer
– (10-20 repetitions of each driver-tyre
combination)
Subjective evaluation
– “Mini circuit”
on highest possible speed
– “blind” testing in badges:
1,2,3 / 2,3,4 / 5,6
– 9 evaluation aspects
+ overall judgement

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

Test week impression

Results Overall Judgement

Influence Tyres on Evaluation Aspects
– +
Yaw delay Steering precision
Stability while
cornering (no throttle
change)
Grip
Steering angle

Correlation Objective Measurements
with Subjective Evaluation
Step steer response time for lateral
acceleration (time delay between 50%
steering angle and 90%
steady state value)

Correlation Objective Measurements
with Subjective Evaluation
Step steer response time for lateral
acceleration

Results puls steer: bandwidth yaw rate
tyre in non linear range?

Workload Measure: High Frequency Area
Indicator for workload: High Frequency Area
area beneath curve fcut-flimit
HFA =
area beneath curve 0-fcut

Results High Frequency Area

Model Based
Driver Parameter Assessment
Two-track model of test vehicle including
lateral load transfer
Tyre model: Magic Formula δ 1
Driver tracking control model = −Kd .
ε prev 1 + τ .s

Optimisation of
Driver Model Parameters Ld and Kd
Cost functional for optimising driver model
parameters Ld and Kd for the different tyres
path error steering rate
weight factor
FC = ∫ (ε ) .dt + wδ .∫ δ .dt
2
() 2
tracking performance workload
Small variation in Ld and Kd
in contrast to non-extreme conditions!
(Monsma: Tyre Technology Int., Annual Review, 2008)

Conclusions & Follow Up
HFA as workload measurement is promising for
correlation with subjective evaluation
Investigation of mental workload for extreme
manoeuvring (heart rate measurements, video)
Driver model parameter adjustment is limited in
extreme manoeuvring conditions in contrast to
non-extreme conditions.
Explore driver parameter adjustment for
relation:
non–extreme conditions subjective evaluation
Workload measurements (and modelling)

Videos test drivers