Overview of the Intelligent Vehicles and Systems Group

See http://controlfreaks.mne.psu.edu for more info

Penn State University

by

Dr. S. Brennan

Overview of the Intelligent Vehiclesand Systems Group

An introduction to Sean Brennan

• Youngest faculty with full appointment in ME, 5th year currently– Graduated from the University of Illinois at Urbana-Champaign

– Experimentalist at heart, focus on chassis dynamics, systems engineering, and control

• Service– Chair of ASME Automotive and Transportation Systems Committee

– National Academies Transportation Visualization Committee

– Organizer for: ASME DSCC conf, IEEE Conf on Control Applications

– Faculty advisor for Penn State Robotics Club, AUVSI Competition

• Teaching:– Department teaching award, 2006, College teaching award in 2007

– SAE Teetor award, 2008

• Research:– $3 million in ongoing research across 6 research labs

– Support 10 to 15 grad students, 10 undergrad researchers

– Selected as top papers at ’07 IFAC Advances in Automotive Control

– Best paper in session, 2007 ASME IMECE

We do vehicle dynamics and control…

That’s me doing ademonstration for myVehicle Dynamics course,Spring 2008!


Advanced estimation and virtual driving…


Robotics and systems integration…


Outline

• Vehicle dynamics

• Advanced estimation

• Robotics and systems


Vehicle Dynamics

Jack-knifing scale vehicles

4 wheel steering High-speed ground robots Passenger vehicle and hybrid vehicle control Heavy Vehicle Reliability

Hybrid-electric militaryvehicles


Full-scale vehicle dynamics testingThe facility:

• PTI Test Track – One of a few closed-access University-owned test-track facilities

– Built to accommodate passenger and heavy vehicles,

– Only facility certified for bus chassis testing

Handling area with100 and 150 ft radius turning

circles

72-inch Rollerchassis

dynamometer

Calibrated pavements anddurability segments

1 mileoval Barrier-free

roadside

Half-mile, 4-lane straight-line segmentfor frequency-responses

Vehicle dynamics and model fitting

“looking under rocks”

Vishi, Sittikorn, John, Bridget, Ryan, Dennis,…

• Experimental testing– We rarely trust other people’s models. Despite many claiming that they are

“rock solid”, it’s the muddy fits that are of interest to us.

– As a consequence, nearly every student in my group is trained in vehicle dynamic validation and data collection


Model FittingFrequency Response – Roll Angle

100

101

-40

-30

-20

-10

0Steering Angle to Roll

w (rad/s)

Mag

(d

B)

100

101

-250

-200

-150

-100

-50

0

50

w (rad/s)

Ph

ase

(deg

)

Data-Large AmpData-Small AmpSprung OnlySprung+UnsprungAsymmetric

Data-Large AmpData-Small AmpSprung OnlySprung+UnsprungAsymmetric


Model Fits – Time Domainlane change

10 12 14 16 18 20-3

-2

-1

0

1

2

3Time vs. Roll Angle

Time (sec)R

oll

(deg

)

measuredSprung+Unsprung+GUnsprung OnlySprung+UnsprungAssymetric

11 12 13 14 15 16 17 18 19 20-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5Time vs. Lateral Velocity

Time (sec)

Lat

eral

Vel

oc

ity

(m/s

)

measuredbicycle+lagSprung+Unsprung+GUnsprung OnlySprung+UnsprungAssymetric

11 12 13 14 15 16 17 18 19 20-25

-20

-15

-10

-5

0

5

10

15

20

25Time vs. Yaw Rate

Time (sec)

Yaw

Rat

e (d

eg/s

)

measuredbicycle+lagSprung+Unsprung+GUnsprung OnlySprung+UnsprungAssymetric

Yaw Rate Roll* Lateral Velocity

Difficulty: easy

* Assuming terrain influence is removed – more later

Difficulty: medium Difficulty: hard


Scale vehicle dynamics – dangerous scenarios“playing with toys”

For many instances of vehicle testing, the use of a full-sized vehicle is costly and dangerous, and yet simulations are onerous and questionable to build

One solution often used is a reduced-scale vehicle.

Mathematics of dimensional analysis allows results to map between behaviors of a scaledvehicle and those of a full-sized vehicle.

Sittikorn, Alexia, Andrew, Janine, Gareth…

Some examples

1:5 scale

wheel-lift characterization

1:14 scale jacknifing 1:8 scale vision-tracking

1:8 scale autonomous motorcycle

1:5 scale

Platooning dynamics

1:5 scale

Multi-input system

- Each axle is independently steered

- Each wheel has independent torque control

0 2 4 6 8 10 12 14 16 18 20-0.5

0

0.5

Po

sitio

n (m

) Measured PredictedReference

0 2 4 6 8 10 12 14 16 18 20-0.5

0

0.5

Ya

w A

ngle

(ra

d) Measured

Predicted

0 2 4 6 8 10 12 14 16 18 20

-0.5

0

0.5

Fro

nt S

tee

r A

ngle

(ra

d)

Time (sec)

Measured Predicted


Comparisons between vehicles…“our hobby is collecting vehicle data”

Sittikorn, Mariona, Haftay, Dennis, Jon

• Use same techniques as used in wind-tunnels, Buckingham Pi Theorem

Comparisons between vehicles…“our hobby is collecting vehicle data”

Sittikorn, Mariona, Haftay, Dennis, Jon

L

a1

L

b2

23 mU

LC f 24 mU

LC r

25 mL

I z

abmI

Index

z

2

Dynamic

25 mL

I z

from publications

from NHTSA database

Outlier data

Advanced estimation

Vehicle-terrain interaction Advanced sensor fusion Redundant estimation

Path of Lidar Sensor

Bridge with cement barriers on either side


The influence of terrainBridget

• Road grade investigated for steady state circle at various speeds

• When aligned based on path distance covered, the road grade measurement is very repetitive irregardless of speed

The influence of terrainBridget

10 12 14 16 18 20-3

-2

-1

0

1

2

3Time vs. Roll Angle

Time (sec)

Ro

ll (d

eg)

measuredmeasured minus low speed rollRoll Model

Because feedback gains are directly related to modeling error, disturbance cancellation enables much higher gains and hence better tracking in closed-loop control.

Terrain as a sensorGPS was never meant to be trusted for feedback control

Adam, Ryan, Vishi

Off-line Localization using Pearson Correlation Coefficient

By comparing pitch disturbances with a terrain map, we are able to resolve longitudinal position as good as

10 cm0 20 40 60 80 100 120 140 160 180-2.5

-2

-1.5

-1

-0.5

0

0.5

1

Distance (m)

Roa

d G

rade

(de

g)

Road Grade Correlation Sample 3

map

data

Max Correlation = 0.9893

Terrain as a sensorLearning as you go…

Adam, Ryan, Vishi

Representative visualization of your work

Real-time Localization using Particle Filters

Tested again at the track: we are able to resolve longitudinal position to 0.5 meters after traveling about 100 meters, with no GPS or other signals

Mapping terrainPramod

The goal of this work is to map road features and thereby correlate results to accident causation and eventually prevention

Impact: 2000+ lives saved a year!

Oct ‘07

Dec ‘07

Nov ‘07

Mar ‘08

Apr ‘08


Mapping terrain

• Shown at right is a banked curve from the test track

• Getting 10 to 30 scans per second out to 80 meters of range.

• Accuracy on the order of 6 cm at best case (perfect GPS).

• Actual error is on the order of a meter or less.


Asphalt Roadway

Example bridge section


Asphalt Roadway

Bridge with cement barriers on either side


Advanced sensor fusion: how to utilize map-based position?

Vishi, Adam

Can get orientation!

•Real and virtual scenes are compared.

•Preliminary results show orientation accuracies of 0.1 deg

Automation and systems integration

4 wheel steeringHigh-speed ground robots Hybrid-electric military vehiclesAutonomous vehicle testing


Solving automation challenges…

• Problem: need standardized interface to measure driver inputs to the steering system and hence tire

Senior project?

• Want to measure driver steering torque and backlash effects caused by steering systems, suspension, tire behavior, etc.

Off-road modelingPreventing the accident in the first place

Bridget, Jason

Currently using Monte-Carlo methods and CarSim to analyze the effect of highway geometry on accident causation


Predicting and preventing unintended roadway departure

• According to FHWA, 60% of vehicle fatalities occurred after leaving the lane

• High-gain control combined with terrain maps gives an unprecedented opportunity to mitigate this through the steering input

Efficiency improvements by sensor fusion“anticipating the road ahead”

Nan, Alexia, Vishi


HEMTT Starter SystemHIL Project

• Army is spending $30 million+ each month on premature battery failure

• Project: ultracapacitor switchover– More reliable starts

– Vastly increase battery life

– Adaptive to extreme environmental temperatures

• (HIL) Test Stand Simulator– Simulates HEMTT engine,

alternator, battery and ultra-capacitor

– Responds to inputs from actual HEMTT starter motor

– Records speeds and torques of starter motor and engine


Why is battery management necessary?

For unmanned vehicles, logistics and support costs are also important, but vehicle runtime and operator safety are paramount (example: EOD bots)

For manned vehicles, reliability is important, but logistics and support costs are huge

Campus-wide hardware-in-the-loop project

The goal of this work is to accelerate hybrid vehicle powertrain development

Distributed Powertrain System

• Utilize campus-wide Ethernet

• Incorporate existing labs

• Integrate with industrial facilities

Faculty Participants: Dr. Sean N. BrennanLab/Center Name: GATE Hardware-in-the-LoopSponsor: DOE

Ultracapacitor

Driving SimulatorBatteryFuel Cell

Chassis DynoIC EngineElectric Motor

Flywheel


Analyzing reliability of PTI bus testing results

• Collecting reliability data from transit agencies around USA

• Comparing transit agency data with PTI test track data to assess their validity

Faculty Participants: Prof. Sean BrennanLab/Center Name: Pennsylvania Transportation InstituteSponsor: Federal Transit Administration

Future Work:

• Develop a predictive failure model to aid transit agencies in making purchase decisions

2.2 2.4 2.6 2.8 3 3.2 3.4 3.6

x 105

0

5

10

15

20

25

30

35

40

45

Distance (in miles)

Fa

ilure

s p

er

last

10

,00

0 m

iles

Falure Rate Distribution of Pittsburgh 5002

Replace Air Compressor 234,026

Replace Trans. - 240,688

Replace Differential - 277,664 Replace Left Rear Axle - 285,520

Replace Trans. (Undefined) - 302,366

Cumulative

Drivetrain

A/C HeatSuspension

Transmission

Allometric Design and Stability Relationships for Explosive Ordinance Robots

Real-time Localization using Particle Filters

•By comparing inertial disturbances with a terrain map, we are able to resolve longitudinal position to 0.5 meters after traveling about 100 meters, with no GPS or other signals

Participants: Brennan, Dean, Logan, Labs: Intelligent Vehicles and Systems Group, ARL, EDOGSponsor: NAVEOD (DoD)

Read more:• Guizzo, Erico. “$280 Million Robot Dustup,” IEEE Spectrum, p. 10-13, Vol. 44, No. 12, North American Edition, December 2007.

New frontiers

• Recently initiated studies on human-vehicle interaction using a recently donated immersive driving simulator

New frontiers: remote semi-autonomy and driver assist

Nan, Alexia, Vishi

Use immersive driving simulator to remotely guide vehicles through pre-mapped terrain.

Thanks to supporters!

The National Science Foundation – funded research into fundamentals of dynamic behavior through several student fellowships. (~$200k)

The National Academy of Science, The Transportation Research Board – funded roadway scanning and terrain modeling (~$300k)

Army TACOM – currently funding HIL work (~$1M)

The Federal Transit Agency – funded test track and vehicle systems used on the track such as the DGPS/IMU system (track ~$14M, current project ~$300k)

Naval Explosive Ordinance Disposal – currently funding robotics work (~$600k)

Questions?

• Vehicle dynamics

• Advanced estimation

• Robotics and systems


Overview of the Intelligent Vehicles and Systems Group

Documents

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