Acknowledgements

10/26/2018

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 Institution Research Labs I‐MAP, University of Florida, USA University of Florida, USA Students Team Mary Jeghers, OTR/L , PhD Student Sherrilene Classen, PhD, MPH, OTR/L, FAOTA, FGSA Sandra Winter, PhD, OTR/L Collaborators Lily Elefteriadou, PhD Luther King, DrOT, CDRS, CDI, OTR/L Dan Hoffman, Assistant City Manager, Linda Struckmeyer, PhD, OTR/L Gainesville, FL Jane Morgan‐Daniel, MLIS, MA, AHIP

Funders AAA NHTSA‐UFTI

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

Sherrilene Classen INTRODUCTION AND GLOBAL PERSPECTIVE: 8‐8.15AM

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Driving

Past Now NowFuture • IADL that requires • IADL that requires ‐ intact visual, cognitive, sensory, and motor functions ‐ giving up personal control ‐ executed in a coordinated fashion ‐ having confidence in technology ‐ in a complex, dynamic, and ‐ having trust in system unpredictable environment ‐ while having control over the • Understanding SAE levels vehicle to steer it cautiously and • Role change safely in the flow of traffic ‐ observing the rules of the road ‐ Driver • Represents an integration of the ‐ Operator person, the vehicle and the ‐ Passenger environment ‐ Dispatcher • A privilege not a right • Be tech savvy • One of the only IADLs that can kill • Understand lingo • A mediator of autonomy, authority, freedom and independence

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

Why? According to NHTSA: Job growth and low fuel prices that led to increased driving, including increased leisure drivingThe and driving USA by young‐ Picture people. 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)

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%) • Vehicle error (10%) • Roadway and • Drowsy driving • 2016 policy AV Infrastructure • Older driver • Safe Cars Saves Lives error (10%) • Distracted driving Recall campaign • Complete • Crash‐avoidance Streets technologies • USDOT FHWA‐ • Automatic emergency infrastructure braking a standard safety projects feature in 99% vehicles by 2022

NHTSA and International Traffic Safety Data and Analysis Group (IRTAD)

Questions

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Sherrilene Classen INTRODUCTION TO AUTONOMOUS VEHICLES: 8.15‐9AM

Automated, Connected and Intelligent Vehicles

Sherrilene Classen, PhD, MPH, OTR/L, FAOTA, FGSA Prof & Chair: Department of Occupational Therapy College of Public Health and Health Professions, UF

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MAJOR DISRUPTION

Opportunity

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.

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

Autonomous Shuttle

Google self‐driving car

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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SAESAE LevelsLevels

Overreliance Disengagement Re‐engagement

Hands off, eyes off, mind off, feet off

https://arcatlantique.its‐platform.eu/activities/sa‐42‐facilitating‐automated‐driving

In‐Vehicle Information Systems (IVIS)‐ SAE Level 0

Technologies that provide information or warnings to 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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Advanced Driver Assistance Systems (ADAS) ‐ SAE Level 1 or 2 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

Automatic emergency braking Crash avoidance Lane departure correction Blind spot detection and correction

Gietelink et al., 2006 Role: Driver MyCarDoesWhat.org, n.d.

• Adaptive cruise control (ACC) • Electric vehicle warning sounds • Glare‐free high beam and pixel • Forward collision warning light • Intersection assistant • Adaptive light control: swiveling • Hill descent control curve lights • Intelligent speed adaptation • Anti‐lock braking system • Lane departure warning system • Automatic parking • Lane change assistance • Automotive navigation system GPS • Night vision with up‐to‐date traffic information • Parking sensor • Automotive night vision • Pedestrian protection system • Blind spot monitor • Rain sensor • Collision avoidance system • Surround view system • Crosswind stabilization • Tire pressure monitoring • Driver drowsiness detection • Traffic sign recognition • Driver monitoring system • Turning assistant • Emergency driver assistant • Vehicular communication systems • Wrong‐way driving warning

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Autonomous Vehicle ‐ SAE Level 3

CAMERAS: Use parallax from LIDAR UNIT: Constantly multiple images to find the distance spinning, it uses laser beams to various objects. Cameras also to generate a 360‐degree detect traffic lights and signs, and image of the car’s help recognize moving objects like surroundings. pedestrians and bicyclists.

RADAR SENSORS: Measure the distance MAIN COMPUTER from the car to (LOCATED IN TRUNK): obstacles Analyzes data from the sensors, and compares its stored Additional maps to assess Lidar Units current conditions.

Role: Driver, Operator NYT, 20 March 2018

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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 vehicles have the capacity of perceiving the environment, and acting in 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 Intelligent Vehicle Symposium, San Francisco, June 2017

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Automated Vehicle • Pros • Cons—6E’s • Safety‐ no driver error • End‐user ‐ Overreliance (not checking blind spots) • People‐access ‐ Misuse (collision avoidance system) • Communities‐ green space ‐ Abuse (drinking and driving) ‐ Disuse (not engaging) • Cities‐ mitigate congestion ‐ Negative transfer knowledge • Environment‐ no emissions • Engineering‐ glitch • Parking spaces‐ repurposed • Education‐ who • Environment‐ potholes, fog The car doesn’t get tired, sleepy, distracted, drunk, or angry… • E‐hacking ‐cybersecurity • Ethical‐ “decisions” https://www.bing.com/videos/search?q=you+tube+v olvo+fail&&view=detail&mid=3FE4C67D785ABC0477 943FE4C67D785ABC047794&&FORM=VRDGAR Fleetwood, J. Ethics, and Autonomous Vehicles. AJPH, 2017

Timeline

https://www.theepochtimes.com/is‐self‐driving‐technology‐already‐making‐us‐safer_2185724.html

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Questions

Luther King AUTONOMOUS VEHICLE CASE STUDY: OLDER ADULT –MRS. WEDDINGTON: 9‐9.10AM

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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 Consistently stops Pedestrian Collision avoidance Perception past stop‐line detection with system audible or haptic feedback

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

Mary Jeghers SCOPING REVIEW: 9.10‐9.25AM

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What is a Scoping Review?

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

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 simulator Use of on-road n = 20 n= 4

Safe Unsafe Safe Unsafe Inconclusive Inconclusive (positive effect) (negative effect) (positive effect) (negative effect) n= 4 n= 1 n= 14 n= 3 n= 2 n= 1

AMS CAW ISA IVWS HUD IVICAS TGA VICS NVES GPS FCW LDW VAIS CSW LCW n= 1 n= 1 n= 1 n= 1 n=4 n=4 n= 1 n= 1 n= 1 n= 3 n= 3 n= 5 n= 1 n= 1 n= 1

Advanced Driver Assistance Systems

Advanced Driver-Assistance Systems (ADAS) n= 5

Use of simulator Use of on-road n= 3 n= 2

Safe Unsafe Comfort Uncomfortable Safe Unsafe (positive effect) (negative effect) (positive effect) (negative effect) (positive effect) (negative effect) n= 3 n= 0 n= 1 n= 0 n= 2 n= 0

LKA ACC BA AS APPS n= 1 n= 2 n= 1 n= 1 n= 1

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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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Sherrilene Classen AUTONOMOUS VEHICLE CASE STUDY: 9.25‐9.45 PM

Case study: First Pedestrian Death Associated with Self‐Driving Car

What went wrong?

Body seen in this area

Elaine Herzberg was struck The self‐driving while walking her bike across Uber was the street somewhere in this traveling north at area, in Tempe AZ about 40 m.p.h.

NYT, 20 March 2018

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Opportunities Task demands; Actions to activate technology What OTs Do Understand occupational performance Analytical skills Person’s ability to the demand of the environment/ vehicle/task • Screening • Assessment • Intervention Goal: Optimize occupational Attitudes & performance (independent Natural & built perception to and safe functioning) of environment person/ people technology Ability to use tech appropriately

Alvarez & Classen, 2017

Facts

Facts from police report Only 1 roof‐mounted lidar sensor • Vehicle compared with 7 lidar units on the • The Volvo XC90 SUV outfitted with older Ford Fusion models 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 https://www.msn.com/en‐us/news/us/uber%E2%80%99s‐ • 45mph zone use‐of‐fewer‐safety‐sensors‐prompts‐questions‐after‐ • 10 PM on Sunday arizona‐crash/ar‐BBKNdBo?li=BBmkt5R • The weather was clear and dry NYT, 20 March 2018

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What went wrong?

Vehicle 1. Sensor detection error How does the vehicle see? Sensors, cameras, 2. Crash avoidance system error lidar or radar (engages when radar and LIDAR agree on obstacle) How does the vehicle think? Algorithms from 3. Algorithm error sensors pares in data acquisition system

Red boxes: cyclist

Yellow boxes: pedestrians

Pink boxes: vehicles Green fences: locations where the car need to slow down

Red fences: locations where the car need to stop

Facts Person • Driver Demented • Uber safety driver was not impaired Drugged • Perception & attitude Drunk • Self driving car…not a driverless Drowsy car Distracted • DDT driver responsibility Disengaged • Situational awareness VTTI‐ 100 person study • Video …eyes off and mind off >2 sec eye glance off road • ~8 sec eye glance off road Strongest predictor of crashes • The Pedestrian • Jaywalking https://www.youtube.com/wa • Pushing her bike tch?v=Cuo8eq9C3Ec • Wearing a dark outfit NTSB investigates Environment Sensor, algorithm, jaywalk, • 45 mph zone pedestrian pushing bicycle, dark • 10 PM on Sunday outfit, SAE level 3, disengaged • The weather was clear and dry driver, eye glance ~ 8 sec NYT, 20 March 2018

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https://www.kqed.org/news/11670355/safety‐agency‐uber‐suv‐detected‐pedestrian‐but‐didnt‐ slow‐before‐fatal‐crash

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)

Results NTSB Investigation

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. v 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”. v 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 P designed to alert the driver. v 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. P Also, the bicycle had no side reflectors and the front and back reflectors were perpendicular to the Uber SUV. E No pedestrian crossing in the vicinity E Uber's driver said she had been monitoring the "self‐driving interface.“ She declined using phone at the time of the crash. P

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Questions

Sherrilene Classen WRAP UP: 9.45‐9.50AM

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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 Projects

Research • 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/

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Q&A

Sherrilene Classen, PhD, MPH, OTR/L, FAOTA, FGSA [email protected] 1.352.2736883