Gastcollege HAN Master Control Systems Engineering

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

Gastcollege HAN Master Control Systems Engineering

by Hans Mestrum on May 27, 2009

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