haptic feedback on the steering wheel to maximize front axle grip

1/28Challenge the future

Haptic feedback on the steering wheel to maximize front axle grip

Joop van GerwenBioMechanical Design & Precision and Microsystems Engineering, Automotive


Contents

• Introduction• Methods

• Concept• Experiments• Data analysis

• Results• Discussion


Introduction


Introduction

• Reduces loss of control • Effect:

• Reduces fatal single vehicle crashes by [1]• 30-50% among cars and

• 50-70% among SUVs

• Best since seat belt!• Developed from ABS• But: large impact on velocity• Active Front Steering (AFS)

ESC systems

[1] S.A. Ferguson, The Effectiveness of Electronic Stability Control in Reducing Real-World Crashes: A Literature Review, 2007

[2] http://www.guy-sports.com/fun_pictures/95-driving_bd.jpg [3] http://static.howstuffworks.com/gif/28002-rollover-accidents-2.jpg


Introduction

• Related research on lateral vehicle dynamics guidance• Lanekeeping

• Principle: shared control• Controller is capable of controlling the system• Actuator power not strong enough for full control

Haptic feedback

[1] J. Switkes, E. Rosetter, I. Coe, Handwheel force feedback for lanekeeping assistance: combined dynamics and stability


Introduction

• Goal :• Use haptic feedback to let the driver take the corrective

AFS action

• Guide to maximum front axle grip

Idea & title explanation


Methods


MethodsConcept – Controller structure

• Purpose:• Guide the driver to the proper steering action


MethodsConcept – Upper controller structure

δ

Desired yaw rate

PD controller

ay

Vx

Limiter

Vx

Tψ_corrective

+ -desψ errorψ

ψ


• Pacejka combined slip tire model

MethodsConcept – Lower controller structure

δ Tfeedback

δrange

Front axle force rangeVehicle state

Optimum steering

angle

Fy_front_axle

(range)

Tψ_corrective

δdes

Feedback torque

calculationδerror

+ -


MethodsConcept – How does it feel?


MethodsExperiments – Vehicle


MethodsExperiments – Tracks

[1] http://maps.google.nl

010203040

50

60

70

80

90100110120130

140

150160 170 180 190

200 210 220230

240

250

260

270280

290300

310320

330

340

350360

370

380390

400410420


• 9 drivers• 2 tracks

• Wet skid-pad• 7 runs of 35 second

• Task: follow inner line as fast as possible

• Adverse track• 3 runs of 70 second (approximately 2 laps)

• Task: take the corners as quick as you can

• NASA Task Load Index• 2 experiment days

MethodsExperiments – Procedures


MethodsExperiments – Pictures & video


Methods

• Filtered: Driver torque and accelerations (3Hz anti-causal low pass)

• Removed: unwanted data• Wet skid-pad• RMS data• Adverse track• Translation of data• One full, running lap isolated

Analysis – Filtering & preparation


Methods

• Performance metrics• Example: velocity

• Driving behavior metrics• Example: steering wheel angle

• Feedback controller metrics• Example: feedback torque

Analysis – Metrics


Results


ResultsSignificance

• Two data sets • T-test to calculate chance that data sets originate

from the same source• Chance < 5% is significant• Influence of external factors

• P-value = 5.1874e-007

0

5

10

15

20

January March

Maximum temperature


ResultsWet skid-pad

0.35

0.4

0.45

0.5

0.55

Wet: FB off Wet: FB on

rms

yaw

rat

e [r

ad/s

]

p-value = 0.035795

0.195

0.2

0.205

0.21

0.215

0.22

0.225

0.23

0.235

0.24


rms

delta

[ra

d]

p-value = 0.0097002

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

8


rms

driv

er t

orqu

e [N

m]

p-value = 5.1645e-005

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035


rms

delta

diff

eren

ce [

rad]

p-value = 0.98408


ResultsAdverse track – Velocity

0 100 200 300 40020

30

40

50

60

70

80

Position on center line [m]

spe

ed

[km

/h]

10th percentile, no FB


10th percentile, FB

90th percentile, FBMean, no FBMean, FBSignificantly lowerSignificantly higher

0 100 200 300 40020

30

40

50

60

70

80


spe

ed

[km

/h]



10th percentile, FB


0 100 200 300 40020

30

40

50

60

70

80


spe

ed

[km

/h]



10th percentile, FB

90th percentile, FBMean, no FBMean, FB

0 100 200 300 40020

30

40

50

60

70

80


spe

ed

[km

/h]

Mean, no FBMean, FB


ResultsAdverse track – One corner

0 100 200 300 400-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6


de

lta [r

ad

]



10th percentile, FB


Steering angle

0 100 200 300 400-1

-0.5

0

0.5

1

1.5


yaw

ra

te [r

ad

/s]



10th percentile, FB


Yaw rate

0 100 200 300 400-15

-10

-5

0

5

10

15

20


dri

ver

torq

ue

[Nm

]



10th percentile, FB


Driver torque

Steering angle error

• Significantly lower• Significantly higher

0 100 200 300 400-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1


de

lta d

iffe

ren

ce [r

ad

]



10th percentile, FB


0 100 200 300 400-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1


de

lta

diffe

ren

ce

[ra

d]



10th percentile, FB



ResultsAdverse track – Road position

0 100 200 300 4000

0.5

1

1.5

2

2.5

3

3.5


La

tera

l po

sitio

n [m

] va

ria

tion

fro

m m

ea

n



0 100 200 300 400-4

-3

-2

-1

0

1

2

3

4


La

tera

l po

sitio

n [m

]



10th percentile, FB



Results

• Variables:• Mental demand• Physical demand• Temporal demand• Performance• Effort• Frustration

• No significant changes• Small test group

NASA Task load index


Discussion


Discussion

• Room for improvements:• State estimation & sensing

• Tire model vs. force sensing bearing

• Haptic feedback philosophy• Desired yaw rate determination very simple

• Corrective yaw torque controller

• Possible negative stiffness of steering system

• Do not prevent drivers from steering back to neutral

Conclusions – Improvements


Discussion

• Haptic feedback caused:• Driving behavior change• Vehicle eager to steer• Higher driver torque• Increased yaw rate• Drivers were drawn to a trajectory

• Potential, but unsafe in current form

Conclusions – Significant changes

haptic feedback on the steering wheel to maximize front axle grip

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