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 methodsto judge driver-vehicle handlingin relationship with tyre characteristics?

Handling, tyre characteristics

� Handling: cornering behaviour+ the driver’s perception

� Tyre characteristics

Tyre characteristics

Construction compound

ply-type

carcass

belt

Dimensionaspect ratio

size

Servicetemperature

wet/dry conditions

Inner pressure

Performance

aligning torque

cornering stiffness

pneumatic trail

peak lateral force coefficient

braking force

coefficient

Aging

wear-in

wear after normal use

slip angle α

V

Fy

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 vehicle performance…

steer by wire

(active)

suspen

sion

electronic stability control

anti-lock bra

king system

traction contro

l

advanced driver assist

system

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)

� 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.

Real life testing

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 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

Real life testing

Assessment Methods to judge(Driver-)Vehicle Handling (3)

= Simulating vehicle behaviour according to the procedures as prescribed in test protocols

– 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

Virtual testing

drivermodels

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

requiredtrajectory

roadconditions

driver

steeringcontrol

throttlebrake

vehicle

road air

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

vibrations, noise,…

disturbances

Closed-loop system

Open-loop system

action

perception

action

perception

Human behaviour and driving tasks

� 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

SRK-model for human behaviour (Rasmussen)

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

requiredtrajectory

roadconditions

driver

steeringcontrol

throttlebrake

vehicle

road air

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

vibrations, noise,…

disturbances

modelled withlinear differential equations

also?

Model the Human Controller

� Describing functions (= approximate transfer functions) of human performance using “control language”

� Can you model human performance by linear 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

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

���� 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

boredom, loss of situation awareness and reduced alertness

overloaded

YH(jω)

McRuer Crossover Model

limitations of the human

reaction time

neuromuscular lag

adjusted to achieve good control

gain

lead

lag

YH

Simulation study

� Will the driver adapt his parameters for different tyres?

� Path tracking

path

Simulation study models

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

yCurrent defined path

= steer speed=d(steer angle)/dt

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

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)

late

ral cu

rren

t err

or

(m)

0 5 10 15 20 25 30 35 40 45

-10

-5

0

5

10

steer speed versus time

time(s)ste

er

sp

eed

(deg

/s)

Cornering stiffness 80%

Cornering stiffness 90%

Cornering stiffness 100% (reference)

Cornering stiffness 110%

Cornering stiffness 120%

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)

late

ral cu

rren

t err

or

(m)

0 5 10 15 20 25 30 35 40 45

-10

-5

0

5

10

steer speed versus time

time(s)ste

er

sp

eed

(deg

/s)

Cornering stiffness 80%

Cornering stiffness 90%

Cornering stiffness 100% (reference)

Cornering stiffness 110%

Cornering stiffness 120%

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 error

gain (%)

Preview orientation

error gain (%)

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 gain (%)

Cost function for different tyre characteristics

0%

50%

100%

150%

200%

250%

300%

350%

80% 90% 100% 110% 120%

Cornering stiffnessJ

sqr(current path error)

weight*sqr(steer workload)

0.0440.66

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 basedon expectedhandling behaviour

� Measured

slip angle [°]

Late

ral

forc

e [

N]

winter all season summer

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 Areaarea beneath curve fcut-flimit

area beneath curve 0-fcut

HFA =

Results High Frequency Area

Model BasedDriver Parameter Assessment

� Two-track model of test vehicle including lateral load transfer

� Tyre model: Magic Formula

� Driver tracking control models

Kd

prev .1

1.

τε

δ

+−=

Optimisation ofDriver Model Parameters Ld and Kd

� Cost functional for optimising driver model parameters Ld and Kd for the different tyres

� Small variation in Ld and Kd

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

( ) ( ) dtwdtFC ...2

2

∫ ∫+= δε δ�

path error weight factorsteering rate

tracking performance workload

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