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10/26/2018
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Autonomous Vehicles for Medically At‐risk Drivers: Opportunities and Challenges
Presenters: Sherrilene Classen, PhD, MPH, OTR/L, FAOTA, FGSA
Luther King, DrOT, CDRS, CDI, OTR/L
Mary Jeghers, OTR/L
Acknowledgements
Academic InstitutionUniversity of Florida, USA
TeamSherrilene Classen, PhD, MPH, OTR/L, FAOTA, FGSA Sandra Winter, PhD, OTR/LLuther King, DrOT, CDRS, CDI, OTR/LLinda Struckmeyer, PhD, OTR/LJane Morgan‐Daniel, MLIS, MA, AHIP
FundersAAANHTSA‐UFTI
Research LabsI‐MAP, University of Florida, USA
StudentsMary Jeghers, OTR/L , PhD Student
CollaboratorsLily Elefteriadou, PhDDan Hoffman, Assistant City Manager, Gainesville, FL
Google: Images
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Outline
• Introduction and driving global perspective (SC) 8.00‐8.15am
• Introduction to autonomous vehicles (SC) 8:15‐9.00am
• Autonomous vehicle case study (LK) 9.00‐9.10am
• Scoping review (MJ) 9.10‐9.25am
• Autonomous vehicle case study – Uber (SC) 9.25‐9.45am
• Wrap up (SC) 9.45‐9.50am
INTRODUCTION AND GLOBAL PERSPECTIVE: 8‐8.15AM
Sherrilene Classen
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Driving
Past Now• IADL that requires
‐ intact visual, cognitive, sensory, and motor functions
‐ executed in a coordinated fashion‐ in a complex, dynamic, and
unpredictable environment‐ while having control over the
vehicle to steer it cautiously and safely in the flow of traffic
‐ observing the rules of the road
• Represents an integration of the person, the vehicle and the environment
• A privilege not a right• One of the only IADLs that can kill• A mediator of autonomy,
authority, freedom and independence
NowFuture
• IADL that requires‐ giving up personal control
‐ having confidence in technology
‐ having trust in system
• Understanding SAE levels
• Role change‐ Driver
‐ Operator
‐ Passenger
‐ Dispatcher
• Be tech savvy
• Understand lingo
Leading Causes of Death World‐Wide
2004 2030
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Global Road Crashes Stats
• Road crashes • kill about 1.3 million • injure 50 million people worldwide every year
• Nine out of 10 lives lost in traffic are in low‐ and middle‐income countries
• The number of road deaths is on the rise even in countries with road safety improvements
• Increase in deaths of vulnerable road user• seniors• pedestrian• cyclists• motorcyclists
WHO, http://www.who.int/mediacentre/factsheets/fs310/en/index1.html
The USA‐ Picture is Bleak
• Road fatalities 2015– 35 092 road fatalities a 7.2% increase over 2014– This is the largest percentage increase recorded in nearly 50 years. – The number of injury crashes and those seriously injured also
increased substantially.
• The fatality rate is 10.9 per 100 000 inhabitants • Pedestrian and cyclists fatalities
– highest in 20 years– motorcyclist deaths increased by over 8%
• Provisional data from the first 9 months of 2016 indicate an additional 8% increase in fatalities over the same period in 2015.
• Cost– USD 242 Billion in 2010– include societal harm then USD 836 Billion in 2010 (6% of GDP)
Why? According to NHTSA:Job growth and low fuel prices that led to increased driving, including increased leisure driving and driving by young people.
NHTSA, 2014
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Road Safety Measures
NHTSA 2017 • Road Safety Management
• Proactive vehicle safety• Automated vehicle technology• Long‐term planning for the road to zero fatalities
• Human error (94%)• Drowsy driving• Older driver• Distracted driving
• Vehicle error (10%)• 2016 policy AV• Safe Cars Saves Lives
Recall campaign• Crash‐avoidance
technologies• Automatic emergency
braking a standard feature in 99%
vehicles by 2022
• Roadway and Infrastructure error (10%)
• Complete Streets
• USDOT FHWA‐infrastructure safety projects
NHTSA and International Traffic Safety Data and Analysis Group (IRTAD)
Questions
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INTRODUCTION TO AUTONOMOUS VEHICLES: 8.15‐9AM
Sherrilene Classen
Automated, Connected and Intelligent Vehicles
Sherrilene Classen, PhD, MPH, OTR/L, FAOTA, FGSAProf & Chair: Department of Occupational TherapyCollege of Public Health and Health Professions, UF
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The development of autonomous vehicle technologies and the fully self‐driving cars, may be the greatest personal transportation revolution since the deployment of the automobile about a century ago.
Opportunity
MAJOR DISRUPTION
Potential of Autonomous Vehicles
• Potential to save 30 000 lives per year, USA
– autonomous vehicles portend the most significant advance in auto safety history
– paradigm shift from minimizing post‐crash injury to preventing collisions
Fleetwood, J. Public Health, Ethics, and Autonomous Vehicles. AJPH, 2017.
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Autonomous Vehicle
Google self‐driving car
Autonomous Shuttle
Fully Automated Vehicle
What is in a name?
Literature • Driverless car• Self‐driving car• Autonomous vehicles• Semi‐autonomous vehicles• Fully autonomous vehicles
Society of Automotive Engineers (SAE)• Partial AV• Conditional AV• Highly AV• Fully AV
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SAE Levels
https://arcatlantique.its‐platform.eu/activities/sa‐42‐facilitating‐automated‐driving
Hands off, eyes off, mind off, feet off
OverrelianceDisengagementRe‐engagement
SAE Levels
In‐Vehicle Information Systems(IVIS)‐ SAE Level 0
Technologies that provide information or warningsto drivers but do not assume functions related to driving tasks
• Back up camera• Front collision warning
‐ haptic‐ auditory‐ visual‐ HUD
Role: Driver
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Vehicle control systems that use environment sensors to improve driving comfort and traffic safety by assisting the driver in recognizing and reacting to potentially dangerous
traffic situations.
These technologies are today’s stepping stones to AV
Advanced Driver Assistance Systems (ADAS) ‐SAE Level 1 or 2
Role: DriverGietelink et al., 2006MyCarDoesWhat.org, n.d.
Automatic emergency brakingCrash avoidance Lane departure correction Blind spot detection and correction
• Adaptive cruise control (ACC)
• Glare‐free high beam and pixel light
• Adaptive light control: swiveling curve lights
• Anti‐lock braking system
• Automatic parking
• Automotive navigation system GPS with up‐to‐date traffic information
• Automotive night vision
• Blind spot monitor
• Collision avoidance system
• Crosswind stabilization
• Driver drowsiness detection
• Driver monitoring system
• Emergency driver assistant
• Electric vehicle warning sounds
• Forward collision warning
• Intersection assistant
• Hill descent control
• Intelligent speed adaptation
• Lane departure warning system
• Lane change assistance
• Night vision
• Parking sensor
• Pedestrian protection system
• Rain sensor
• Surround view system
• Tire pressure monitoring
• Traffic sign recognition
• Turning assistant
• Vehicular communication systems
• Wrong‐way driving warning
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• Adaptive cruise control (ACC)
• Glare‐free high beam and pixel light
• Adaptive light control: swiveling curve lights
• Anti‐lock braking system
• Automatic parking
• Automotive navigation system GPS with up‐to‐date traffic information
• Automotive night vision
• Blind spot monitor
• Collision avoidance system
• Crosswind stabilization
• Driver drowsiness detection
• Driver monitoring system
• Emergency driver assistant
• Electric vehicle warning sounds
• Forward collision warning
• Intersection assistant
• Hill descent control
• Intelligent speed adaptation
• Lane departure warning system
• Lane change assistance
• Night vision
• Parking sensor
• Pedestrian protection system
• Rain sensor
• Surround view system
• Tire pressure monitoring
• Traffic sign recognition
• Turning assistant
• Vehicular communication systems
• Wrong‐way driving warning
Autonomous Vehicle ‐ SAE Level 3
LIDAR UNIT: Constantly spinning, it uses laser beams to generate a 360‐degree image of the car’s surroundings.
CAMERAS: Use parallax from multiple images to find the distance to various objects. Cameras also detect traffic lights and signs, and help recognize moving objects like pedestrians and bicyclists.
MAIN COMPUTER (LOCATED IN TRUNK): Analyzes data from the sensors, and compares its stored maps to assess current conditions.
Additional Lidar Units
RADAR SENSORS: Measure the distance from the car to obstacles
NYT, 20 March 2018Role: Driver, Operator
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Connected Vehicle ‐ SAE Level 4&5
A connected car is a car that is equipped with internet access and with a wireless local area network. The car can share internet access with other devices both inside and outside the vehicle.
Role: Operator, Passenger
Intelligent Vehicle – SAE Level 4&5
Intelligent Vehicle Symposium, San Francisco, June 2017
• Intelligent vehicles have the capacity of perceiving the environment, and actingin response to that environment, without the help of a human being.
• These systems ‐ learn from experience, security, connectivity‐ adapt according to current data
Data managed to provide algorithms for vehicle output responses
Role: Passenger, Dispatcher
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Automated Vehicle
• Pros
• Safety‐ no driver error
• People‐access
• Communities‐ green space
• Cities‐mitigate congestion
• Environment‐ no emissions
• Parking spaces‐ repurposed
• Cons—6E’s
• End‐user‐ Overreliance (not checking blind spots)
‐ Misuse (collision avoidance system)
‐ Abuse (drinking and driving)
‐ Disuse (not engaging)
‐ Negative transfer knowledge
• Engineering‐ glitch
• Education‐ who
• Environment‐ potholes, fog
• E‐hacking ‐cybersecurity
• Ethical‐ “decisions”
Fleetwood, J. Ethics, and Autonomous Vehicles. AJPH, 2017
The car doesn’t get tired, sleepy, distracted, drunk, or angry…
https://www.bing.com/videos/search?q=you+tube+volvo+fail&&view=detail&mid=3FE4C67D785ABC0477943FE4C67D785ABC047794&&FORM=VRDGAR
Timeline
https://www.theepochtimes.com/is‐self‐driving‐technology‐already‐making‐us‐safer_2185724.html
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Questions
AUTONOMOUS VEHICLE CASE STUDY:OLDER ADULT – MRS. WEDDINGTON: 9‐9.10AM
Luther King
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Macular Degeneration
• Very common amongst older adults 60 and over
• Leading cause of vision loss
• Affects 10 million Americans
• Blurred vision – key symptom
• Progressive loss of central vision
• Exudative AMD – wet form
• Nonexudative AMD – dry form
American Macular Degeneration Foundation, n.d.
Mrs. Weddington’s Personal History
• 69‐year‐old African American female • Resides in Gainesville, FL• Widowed• Bachelor’s degree in advanced sonography• Independently owns and runs a small ultrasound clinic
• Enjoys exercising at the gym in her apartment complex
• Enjoys driving to Jacksonville, FL to spend time with friends and family
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Mrs. Weddington’s Medical History
• Recently diagnosed with non‐exudative (dry‐form) Age‐related Macular Degeneration (AMD) at right eye at the intermediate stage with mild vision loss.
• AMD at left eye at early stage with no vision loss• Co‐morbidities
‐ High blood pressure (24 years)‐ Diabetes mellitus Type II (27 years)‐ Congestive heart failure (12 years) ‐ Myocardial infarction (11 years)‐ Coronary artery bypass graph (9 years)‐ Hypercholesterolemia (12 years) ‐ Arthritis at all joints (7 years)‐ Right hip arthroplasty (2 years ago)
Mrs. Weddington’s Medical History
• Current medications
‐ Vitamins C and E (AMD)
‐ Beta‐carotene (AMD)
‐ Lisinopril (blood pressure)
‐ Metoprolol (CHF)
‐ Atorvastatin (hypercholesterolemia)
‐ Aspirin (arthritic pain)
‐ Wears prescription glasses
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Mrs. Weddington’s Functional History
• Independent in all ADLs
– lives in a 1st floor apartment
– had bathroom renovated prior to right hip arthroplasty: walk‐in tub
• Independent in all IADLs
– enjoys duplicating meals that she sees on the “Food Network”
– currently conducting a new‐hire search for an Ultrasound Technician to assist her with the clinic
Mrs. Weddington’s Driving History
• Licensed since the age of 16
• Reports no accidents, tickets, or crashes in the past 5 years
• Reports no refresher courses taken
• Reports 2 “near misses” in the past 3 months
– 1 involving a ball rolling out into the street
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Mrs. Weddington’s Driving History
Driving habits• Typically drives during the day• Work
‐ 1.5 miles away from home‐ client’s may be seen on an on call basis
• Visits family and friends in Jacksonville, FL on weekends‐ 1.5 hour drive
• Avoidance strategies‐ night‐ inclement weather
• Additional information‐ reports not trusting Uber drivers‐ reports hearing about new vehicle technology that may keep her
driving longer
Mrs. Weddington – AMD
Reason for referral• Refused surgical intervention• Ophthalmologist talked with her about vision changes and future impact on driving– distinguishing dark cars on dark roads– difficulty with visual acuity on cloudy days– identification of traffic signs and signals
• Subjective information on driving difficulties – “Every once in a while I notice that I stop too close to the car in front of
me”– “With all the UF students around town riding scooters and bikes I notice
that I feel nervous when driving”
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Clinical Test Results
Vision – Corrective lenses• Date of last eye exam 26 July 2018
• Acuity for both eyes (20/40) ‐ right eye 20/60
‐ left eye 20/40
• Contrast sensitivity (intact)
• Peripheral fields, 140 degrees (intact)
• Depth perception, 5/9 (borderline, cut‐off = 5/9)
• Color discrimination, 6/8 (intact, cut‐off = 6/8)
• Lateral/vertical phorias (intact)
Florida Dept. of Highway Safety and Motor Vehicles, n.d.Optec 5000 Series Tester Manual, 2018
Clinical Test Results
Cognition
• Mini Mental State Examination, 30/30 (WFL)
‐ cut‐point 26/30
• Trails B, 166 seconds (WFL)
‐ cut‐point 180 seconds
• UFOV, Category 2 (Low risk for crashes)
‐ sub‐test 1: 32.1 ms
‐ sub‐test 2: 45.7 ms
‐ sub‐test 3: 400.1 ms (cut‐point, 500 ms)
Driving and Community Mobility: Occupational Therapy Strategies Across the Lifespan, Chapter 9 (2012)
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Clinical Test Results
Motor
• Independent in transfers and ambulation
• ROM WFL at all joints except neck
‐ restricted passed 30 degrees on right side
• Strength WFL at all extremities
• Coordination
‐ Finger to nose: R=6.3 sec; L=5.9 sec (Cut‐off, 10 sec)
‐ Toe tap: R=8.1 sec; L=7.8 sec (Cut‐off, 10 sec)
Classen et al., 2015 Molnar et al., 2007
On‐Road Test Results
• Decreased brake reaction time
• Consistently stops past stop‐line
• Tailgating
• Drives 10 miles per hour below speed limit on interstate
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How can AV help?
Clinical Assessment Driving Errors IVIS ADAS
Borderline Depth Perception
Consistently stops past stop‐line
Pedestrian detection with audible or haptic feedback
Collision avoidance system
How can AV help?
• Challenges:– cognitive workload to manage IVIS and ADAS systems
– increase in distraction
– may not see warnings from IVIS
• Potential benefits:– assist
• audible and haptic feedback
• enhanced brake reaction
– improve • driving performance and safety
• comfort
• convenience
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Questions
SCOPING REVIEW: 9.10‐9.25AM
Mary Jeghers
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A type of evidence‐informed review:
• Exploratory research question• Maps key concepts• Clarifies definitions• Establishes evidence sources• Finds research gaps• Identifies implications for research, practice, or policy
What is a Scoping Review?
Arksey & O’Malley, 2005
Research Question
Based on the English literature what is the impact –convenience, comfort, safety – of IVIS and/or ADAS on the
driving task of adults 65 years of age and older?
Convenience Comfort Safety
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In‐Vehicle Information Systems
In-Vehicle Information Systems (IVIS)
n= 24
Use of simulatorn = 20
Use of on-roadn= 4
Safe
(positive effect)
n= 14
Unsafe
(negative effect)
n= 3
FCWn= 3
Inconclusive
n= 4
Safe
(positive effect)
n= 2
Unsafe
(negative effect)
n= 1
GPSn= 3
LDWn= 5
VAISn= 1
CSWn= 1
LCWn= 1
VICSn= 1
TGAn= 1
IVICASn=4
HUDn=4
IVWSn= 1
CAWn= 1
ISAn= 1
NVESn= 1
AMSn= 1
Inconclusive
n= 1
Advanced Driver Assistance Systems
Advanced Driver-Assistance Systems (ADAS)
n= 5
Use of simulatorn= 3
Use of on-roadn= 2
Safe
(positive effect)
n= 3
Unsafe
(negative effect)
n= 0
Safe
(positive effect)
n= 2
Unsafe
(negative effect)
n= 0
APPSn= 1
ACCn= 2
BAn= 1
LKAn= 1
ASn= 1
Comfort
(positive effect)
n= 1
Uncomfortable
(negative effect)
n= 0
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Take Home Messages
• Impacts of IVIS– positive: enhanced safety (e.g., faster response)
– negative: cognitive workload increase, over‐reliance
• Impacts of ADAS – positive: enhanced safety and comfort (e.g., speed control, lane
maintenance, levels of stress decreased or maintained)
• Unable to determine impact of IVIS and/or ADAS on convenience
• Implications for program development to inform Smart Features for Older Drivers version 3
Classen, S., Jeghers, M., Morgan‐Daniel, J., Winter, S., King, L., & Struckmeyer, L. Smart in‐vehicle technology and older drivers: A scoping review. Manuscript submitted on July 31st, 2018 to OTJR: Special Edition on Artificial Intelligence, Robotics, and Automation.
Questions
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AUTONOMOUS VEHICLE CASE STUDY: 9.25‐9.45 PM
Sherrilene Classen
Body seen in this area
The self‐driving Uber was traveling north at about 40 m.p.h.
Elaine Herzberg was struck while walking her bike across the street somewhere in this area, in Tempe AZ
Case study: First Pedestrian Death Associated with Self‐Driving Car
NYT, 20 March 2018
What went wrong?
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Opportunities
What OTs DoUnderstand occupational performanceAnalytical skillsPerson’s ability to the demand of the environment/ vehicle/task• Screening• Assessment • Intervention Goal: Optimize occupational performance (independent and safe functioning) of person/ people
Alvarez & Classen, 2017
Attitudes & perception to technology
Natural & built environment
Task demands; Actions to activate technology
Ability to use tech appropriately
Facts
Facts from police report• Vehicle
• The Volvo XC90 SUV outfitted with sensor system (not computer vision)
• In autonomous mode• Speed 40mph• The car did not slow down
• Person• Neither the Uber safety driver nor
the pedestrian was intoxicated • Pedestrian was not in a pedestrian
crossing• Pedestrian wore dark outfit• Pedestrian pushed her bike
• Environment• 45mph zone• 10 PM on Sunday• The weather was clear and dry
NYT, 20 March 2018
Only 1 roof‐mounted lidar sensor compared with 7 lidar units on the older Ford Fusion models
https://www.msn.com/en‐us/news/us/uber%E2%80%99s‐use‐of‐fewer‐safety‐sensors‐prompts‐questions‐after‐arizona‐crash/ar‐BBKNdBo?li=BBmkt5R
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What went wrong?
Vehicle
How does the vehicle see? Sensors, cameras, lidar or radar
How does the vehicle think? Algorithms from sensors pares in data acquisition system
Yellow boxes: pedestrians
Red boxes: cyclist
Pink boxes: vehicles Green fences: locations
where the car need to slow down
Red fences: locations where the car need to stop
1. Sensor detection error2. Crash avoidance system error (engages when radar and LIDAR agree on obstacle)
3. Algorithm error
Facts Person
• Driver• Uber safety driver was not impaired• Perception & attitude
• Self driving car…not a driverless car
• DDT driver responsibility• Situational awareness
• Video …eyes off and mind off• ~8 sec eye glance off road
• The Pedestrian• Jaywalking• Pushing her bike• Wearing a dark outfit
Environment• 45 mph zone• 10 PM on Sunday• The weather was clear and dry
NYT, 20 March 2018
DementedDruggedDrunkDrowsyDistractedDisengaged
NTSB investigatesSensor, algorithm, jaywalk, pedestrian pushing bicycle, dark outfit, SAE level 3, disengaged driver, eye glance ~ 8 sec
https://www.youtube.com/watch?v=Cuo8eq9C3Ec
VTTI‐ 100 person study> 2 sec eye glance off roadStrongest predictor of crashes
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View of Uber self-driving system data playback at about 1.3 seconds before the Volvo SUVs struck Herzberg. At this point, the vehicle's self-driving system had determined an emergency braking maneuver would be needed to mitigate a collision. Yellow bands are shown in meters ahead. Orange lines show the center of mapped travel lanes. The purple shaded area shows the path the vehicle traveled, with the green line showing the center of that path. (National Transportation Safety Board)
https://www.kqed.org/news/11670355/safety‐agency‐uber‐suv‐detected‐pedestrian‐but‐didnt‐slow‐before‐fatal‐crash
NTSB says the autonomous Uber that struck and killed Herzberg spotted her about 6 seconds before hitting her, but didn't slow down because the vehicle's built‐in emergency braking feature was disabled.
Emergency braking maneuvers are not enabled while Uber's cars are under computer control. That's a measure designed "to reduce the potential for erratic vehicle behavior”.
Herzberg wore dark clothing and did not look in the direction of the vehicle until just before impact. A toxicology report showed that she tested positive for methamphetamine and marijuana.
Uber's driver said she had been monitoring the "self‐driving interface.“ She declined using phone at the time of the crash.
Also, the bicycle had no side reflectors and the front and back reflectors were perpendicular to the Uber SUV.
Results NTSB Investigation
Uber's autonomous driving system "relies on an attentive operator to intervene if the system fails to perform appropriately during testing." The system is not designed to alert the driver.
v
v
P
E
P
Pv
No pedestrian crossing in the vicinity E
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Questions
WRAP UP: 9.45‐9.50AM
Sherrilene Classen
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Major Points
Threat to public health AND consumer trust, confidence and adoption• USA roads are used as live laboratories!
• Technology is not ready
• People using the technology—or interacting with the technology‐‐ are not ready
• Understanding the person‐vehicle‐environment interaction is insufficient
• Unless corrected—we can expect more injuries and fatalities
UF OT ProjectsResearch • UF Older Driver AV Demonstration Project • Scoping Review• AAA Smart Features version 3• https://mobility.phhp.ufl.edu/
Clinical Practice• SmartDriver Rehab Services• https://ufhealth.org/uf‐smartdriver‐rehab
Education• Certificate in Driver Rehabilitation Therapy• https://drt.ot.phhp.ufl.edu/
