The pathway to selfdriving vehicles:
Disconnects between
human capabilities
and advanced vehicle
systems?
Bryan Reimer, Ph.D.

MIT AgeLab & New England University Transportation Center
JITI Self-Driving Cars Seminar
Osaka, Japan
October 21,
21 2013

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

The Ever Changing Vehicle• OverO e tthee past 100
00 oro so years,
yea s, whilee thet e outward
out a d appearance
appea a ce ofo vehicles
e c es has
aschanged, we have seen little change in how drivers interface with the vehicle.
• What do trends in advanced driver assistance systems, automation andinformation connectivity tell us about expectations for the next 100 years?

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Benefits of Vehicle Automation“Autonomous cars may seem like a gimmick, hebegins but when you consider all the time thatbegins,people won’t be devoting to their rear view mirrors,
and all the efficiencies that come from cars that
couldld beb zippingi i betweenb terrands
d ratherth thanth idling
idliin parking lots, the world looks like a very different
place. Car ownership would be unnecessary,because your car (maybe shared with yourneighbors) will act like a taxi that’s summoned
when needed. The elderly and the blind could bethoroughly integrated into society. Traffic deaths
could be eradicated. Every person could gain losthours
ou s backbac foro working,og, reading,ead g, ta
talking,g, oor
searching the Internet.”
Google co‐founder Sergey Brin as reported by Brad Stoneoff Bloomberglb
Business Weekk – May 22, 2013
Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Technological AdvancesWill lead to driverless vehicles but challenges remain•

SSensortechnology
t h l

•

Computational power•

Algorithm development•

Connectivity

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Vehicle Automation

National Highway Traffic Safety Administration•

Level 0 – No Automation•

Level 1 – Function Specific Automation•

Level 2 – Combined Function•

Level 3 – Limited Self-Driving Automation•

Level 4 – Full Self-Driving Automation

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Levels of Control“Partially Autonomous Driving” is the focus of todays
talk• Level 0 – No Automation• Level 1 – Function Specific Automation• LevelLl 2 – Combined
Cbi d F
Functioni• Level 3 – Limited Self-Driving Automation

Key area of focus• Level 4 – Full Self-Driving Automation

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Human Centered ConsiderationsA partial list in no particular order of significance• Trust in technology• The theory of experience• Education• Failures in automation• Social / political expectations• Workload

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

My Trust in Technology

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Automation and the Big Red
ButtonTo Trust or Not?
• In many situations
automation will outperform
human operation
operation, but will
the driver trust it?
• How will one choose whento or when not to provide /accept autopilot control?
• Experiential learning does
not yet exist.

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Experience
Vehicle Miles Traveled (VMT)()
Vehicle Miles Driven (VMD)

Today
VMT = VMD

Tomorrow?
VMT ≠ VMD

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

A Case Study: The FAA

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

A Simple Way to Think ofOperator Behavior VariabilityDrivers

PilotsAstronauts

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Motivation to Learn and Maintain
Focus
Drivers

PilotsAstronauts

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Education“One of the myths about theimpact of automation onhuman performance is asinvestment in automation
increases, less investment isneeded in human expertise”(David Woods as quoted byRobert Sumwalt, 2012)

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Failures in Automation
Required reading”There will always be a set ofcircumstances that was notexpected, that the automation
either was not designed to handle
or other things that just cannot bepredicted,” explains (Raja)
Parasuraman. So as system
reliability approaches—but doesn’tquite reach—100 percent, ”themore difficult it is to detect theerror and recover from it”
Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Social / Political Forces WorryMe!

Flying robots with and without
missiles worry many

Aviation is safer than driving but wefrequently feel less secure

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Workload & Performance
Yerkes-Dodson Law
The relationship between performance and physiological ormental arousal

P
Performa
ance

OptimalRange

Inattention

ActiveDistraction

Fatigue

Overload
Workloadooad / StressSt essCorrespondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Workload & Performance
More Information in the Vehicle Tends to Increase Workload

PPerforma
ance

OptimalRangeInattention

ActiveDistraction

Fatigue

Overload
Workloadooad / StressSt essCorrespondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Workload & Performance
Automation Tends to Lower Workload

P
Performa
ance

OptimalRange

Inattention

ActiveDistraction

Fatigue

Overload
Workloadooad / StressSt essCorrespondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Physiological Arousal
What Can We Study in the Car?

Part of a larger project evaluating various methods of detecting
driver stateMMeasuresinitiallyi i i ll considered:idd› Heart Rate
› Heart Rate Variability› Pulse height (peripheral blood flow)› Skin Temperature› SkinSki ConductanceC d t› Skin Conductance Response› Respiration Rate
› Pupil diameter› Muscle Tension› EEG (brain waves)› Stress Hormones

drawn in part from Mehler et al., 2009

› fNIRS (brain blood flow)

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Driver State Detection

Classification of Driver Workload / Arousal•

At the group level, changes
in demand are clearly evident
across several features•

Can machine learning beused for detection at theindividual level?
› Apply sliding window to generatea feature set› Use classic approaches such assupport vector machines (SVN),
neural networks or nearest
neighbor to classify state

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

UnanticipatedpConsequences
qFailure is not an option

1.

Driverless car accident that results in loss of life2.

Major media coverage

3.

Public outcry and fear of automation limits usey (level(1)) systems
yof active safety

4.

Push for expedited regulation that may result ininefficient standards

5.

Setbacks in auto safety could last for years

66.

BBenefits
fit off Level
Ll 4 autonomy
tdelayedd ld

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

In Summary, I Believe We NeedTo:
• Continue exploring technologies forautonomous vehicles• Make parallel investments in developing
our understanding of how to optimize thehuman’s connection with autonomoussystems• Clarify the benefits and consequences ofsystem use and misuse• Learn from complementary domains• Stop assuming that autonomy alone willsolve our nation’s transportation problems

Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013

Contact
Bryan Reimer, Ph.D.
Bryan Reimer, Ph.D., is a Research Engineer in the Massachusetts
Institute of Technology AgeLab and the Associate Director of theNew England University Transportation Center. His research seeks
to develop new models and methodologies to measure andunderstand human behavior in dynamic environments utilizing
physiological signals, visual behavior monitoring, and overall
performance measures. Dr. Reimer leads a multidisciplinary team ofresearchers and students focused on understanding how drivers
respond to the increasing complexity of the operating environmentand on finding solutions to the next generation of human factors
challenges
h ll
associatedi d withi h distracted
didddriving,i i automationi andd otherhin‐vehicle technologies. He directs work focused on how drivers
across the lifespan are affected by in‐vehicle interfaces, safetysystems, portable technologies, different types and levels ofcognitive load. This research also assesses the impact of medical
iimpairments
it suchh as didiabetes,b t cardiovascular
dil didisease, ADHD anddautism. Dr. Reimer is an author on over 80 peer reviewed journaland conference papers in transportation. Dr. Reimer is a graduate of
the University of Rhode Island with a Ph.D. in Industrial and
Manufacturing Engineering.

reimer@mit.edu(617) 452-2177
http://web.mit.edu/reimer/www/
Correspondence > Bryan Reimer, Ph.D. > (617) 452 – 2177 > reimer@mit.edu© 2013