Autonomous Vehicles Betsey Tramonte and John Broemmelsiek U.S. Department of Transportation - PowerPoint Presentation

Autonomous Vehicles Betsey Tramonte and John Broemmelsiek U.S. Department of Transportation Federal Highway Administration – Louisiana Division November 15, 2017

Presentation Overview Intelligent Transportation Systems Connected Vehicle Environment Autonomous Vehicle Environment Market Opportunity and Risks Cyber Security Road Confusion Research

What is ITS? Information and communications technology to manage and operate surface transportation systems . System Control Decision Information Transaction Automation

Federal Highway Administration (FHWA) Federal Motor Carrier Safety Administration (FMCSA) Federal Transit Administration (FTA) Federal Railroad Administration (FRA) National Highway Traffic Safety Administration (NHTSA) Maritime Administration (MARAD). ITS Joint Program Office The ITS JPO has Department-wide authority in coordinating the ITS program and initiatives among the following DOT Offices:

Presentation Overview Intelligent Transportation Systems Connected Vehicle Environment Autonomous Vehicle Environment Market Opportunity and Risks Cyber Security Road Confusion Research

Transportation Challenges Safety 35,092 highway deaths in 2015 6.3 million crashes in 2015 Leading cause of death for ages 4, 11–27 Mobility 5.5 billion hours of travel delay $121 billion cost of urban congestion Environment 2.9 billion gallons of wasted fuel 56 billion lbs of additional CO 2

So l v i n g Transportation Problems Improving s afetyReduce and mitigate crashesIncreasing mobili ty and accessibil ityExpand capa city of roadway infra structureEnhance traffic flow dynamicsMore personal mobi lity options for dis abled and aging population Reducing energy use and e m i ss ions A erod y na m ic “dra ft i ng” I m pro v e t raffic flow dynamics…but connectivity is critical to achieving the greatest benefits U .S. Department of Transportation ITS Joint Program Office 7

Connec t e d Veh i c le Pilot Deployment ProgramProposed Pr ogram ScheduleSeptember 2015September 2016 September 20 20Wave 1 Pilot Deployments A ward(s)Wave 2 Pilot Deployments Award(s)Pilot Deploy ments CompleteRes ourcesITS JPO W ebsite: http://www.its. d ot . g o v / CV Pilots Pro g ram W e b site: h ttp:// w ww.its . dot.gov/pilotsCV Pilot Program Goals

Connected Vehicle Concept – US DOT Video

Conn ected V e h i cle Milestones U .S. Department of Transportation ITS Joint Program OfficeAugust 2014: NHTSA ANPRM on vehicle-to-vehicl e communications May 2015: Se cretary Foxx announces V2V rulemaking acceleration S ummer 2015: FH WA V2I guidance documentFal l 2015 : F i rst w a ve o f C V P il o ts to beg i nEnd of 2015: V2V NPRM interagency reviewMandated connected vehicle technology NPRM recently completed comment period

Presentation Overview Intelligent Transportation Systems Connected Vehicle Environment Autonomous Vehicle Environment Market Opportunity and Risks Cyber Security Road Confusion Research

Connected and Automated Vehicles Connected Automated Vehicle Leverages autonomous automated and connected vehicles Connected Vehicle Communicates with nearby vehicles and infrastructure Not automated (level 0) Autonomous Automated Vehicle Operates in isolation from other vehicles using internal sensors

NHTSA Levels of Automation

NHTSA Levels of Automation C/NET VIDEO on NHTSA and SAE Levels of Automation

US DOT Federal Automated Vehicle Policy Vehicle Performance Guidance for Automated Vehicles Model State Policy NHTSA’s Current Regulatory Tools New Tools and Authorities

Market Opportunity and Risk VIDEO

Presentation Overview Intelligent Transportation Systems Connected Vehicle Environment Autonomous Vehicle Environment Market Opportunity and Risks Cyber Security Road Confusion Research

Increasing P ene tration o f Assisted Driver Features 16% 21% 30% 42% 22% 25%36% 46% 26%30% 41% 50%Lane Departur e Warning S y s tem Collisio n Avo i d a n ce / A l ert System B lind Spot Monitoring / Warning SystemPark Assist / Backup Warning System C onsumer Understanding of Tech n o l ogy C o nt ai n e d in Thei r V e h icle - Indu s try 2 0 14 2 0 15 2 0 16

An even larger percentag e want Assisted Driver features 3% 18% 31% 23% 46% 32%45%69% 71% 71% 82%87% Collision P r o t e cti o n F e a t u r e s in V ehic l e – Industry% Have this feature% Want this featureAutoma t ic P ar k i n g System Lan e - K ee pin g System Ad a p t i v e C r ui s e Con t r o l Lo w -Spee d Col l i s i o n Avoidance P ark As s ist Bl in d S p o t W a r nin g and Detection

Risk o f use r confusion – Industry variations Marketi ng namesAcronymsIconsDefault conditionAbi lity to turn sys tem offLast state remembr anceOperational characteristicsCommunication m ethodCustomiza tion20

Risk o f user confus ion – Cruise ControlAd aptive Cruise Co ntrol (ACC)Advanced Smart Cruise Control (ASCC)Aut omatic Cruise Cont rol (ACC)Acti ve Cruise Contr ol (ACC)Cooperative Adaptive Cruise Control (CAC C) Intelligent Cruise Control (IC C)Dynamic Radar C ruise Co n t r ol (D R C C)  ….

Lo w pri or experience highl ights learning method need 10% 10% 25% 21% 15% 36 % 35%39% 39%30% 35%35% 31% 36% 28% Low- S pee d Collision Avoi d a n ce S y s tem La n e - Ke e pi ng/Centering SystemBlind Spot Warning and DetectionAdaptive Cruise Co ntrol Park Assist Lear n e d H o w t o Ope r a t e - Indu s t r y O wn er's ma n ual “ T oo k me 6 w ee ks to e v e n k n o w h o w to u se . The fac t ory d e fa ult w a s s e t to of f!!! A nd t h e de a l e r d i dn ’t e x p l a in t h e fe a tu r e to m e ” “ H a d to r ea d o wn e rs m a n u a l d u e to v ery limit e d guid a nce fro m d ea l e r s hip” “ Tri a l a nd error with the controls to figure out how it worked” Prior experience with the feature D e ale r s t a f f e x p lai n e d t h e featu re

Bl in d Spo t Warning and Detection69% of consumers who have this use it every time they drive

O f tho s e c onsu m ers that had a Blind Spot Monitoring Problem…51% Happens OccasionallyInconsistent performance can erode trust False Positive: 41% False Negati ve: 23%Doesn’t W ork at All: 45%When it rains or is damp out th e side warni ng light goes o n i nd i c at i n g a c ar is appr o a c h i ng me from the drivers side but there are no cars”““Of ten indicates a car is in the blindspot w h en th ere is not . O r , o f t en do es not re c ogn i ze sma ll c ars in th e b li n d s po t, es p e c i a lly t h e Hond a Civi c ” T h e b li n d spot m on i t o r in g w ill f a il w ith a b ee p an d w ar n i n g mess ag e o n th e ce n t er c onsol e that A u d i sid e assi s t is not a v a il ab le. It c annot b e tur n ed ba c k o n u n t il t h e vehicle is s hu t o ff and restarted…EXTREMELY dangerous when you expect it to be there w hen making lane changes and it's not there”Intermittently trips off. Lane departur e, blind spot monitoring trips off w ith it an d r a ndo mly c ut s ba c k in 10 to 20 minutes later. Dealer blamed protective front end film on sensors which was removed. Random intermittent trips still occur.” Industry Verbatims “ “

Tr u s t in techn ology Conc erns surrounding add ed technology complexities, privacy, and the potential for systems to be hacked, “hijacke d,” or crash ar e prominent across all respondents.Gen Y and Z are nearly twi ce as likely as Gen X and five times as likely as Baby Boomers to t r u s t ful ly au t om a t ed, s el f- dri ving t echnolog y.There is a relationship between consumer interest in Full Self-Driving Automation and their trus t in the technology.To achieve serious v o l u m e, Ful l Sel f - Dr i ving Au t om a t i o n s houl d f o c u s o n the c ohort s mo s t a cc e p t i n g o f i t and a l l o w the o l der g ene r a t i on s mo r e time and e xpo s u r e t o i ts b e ne fit s and mar k e t r ea dine s s .

4 T h e t echno l ogies that are the building blocks of AV technology are here today.So what’s the hold up?Timing of Autonomous Technology

E na c ted Legi s lation Executive Order DE MD DC MAORRI CTNJVTNH ME PAWV NC SC GAFL O H MI IN IL WI IA MO LA NM CO SD ID CA WA TN M S AL MT ND NV AZ UT WY NE KS O K TX AR MN KY VA NY AK HI Sou r c e : N a t i ona l C oun c i l o f S t a t e Leg i s l a t u r e s , Au t ono m o u s / S e l f - D r i v i n g Leg i s l a t i o n , 6 / 6 / 1 6 Regulatory Environment

201 6 AA A Su rvey 75% of drivers are scared of self-driving carsAdoption of Autonomous Technology

12 “In the r ea l world there are always unknown mov ing obstacle s . . . it’s alwa ys possible to find situations where a collision wi ll happen.”Thi erry Fraich ardPro j e c t - T e a m P RI MA , 2014 “ A s the Google AV was reentering the center of the lane it made c ontact with the side of the bus. The Google A V w a s op era ting in a utono m ous m ode a nd t r a v e ling a t l es s th a n 2 m ph, a nd the bus w a s t r a v e lling a t a bout 1 5 m ph a t the ti m e of c ont ac t.” Cali f o rni a D ep a r tme n t of M otor V e hicl e s , G o o g l e Accid e n t R epo r t, 2016 No guarantees

“If I h a d asked pe ople what they wanted, they would have said fas ter horses.” – Henry Fo rd30Image: Nantucket Histo ric al Associ ation Lib raryHistorical Public Opinion

Large-scale Adoption will be Disruptive

Toda y you r ca r is an unused asset 95% of the day.Will w e own fe wer cars if t hey are avail able on- demand?Will you hire-out your ca r? Or send i t on errands whe n it’s not i n u s e ? Sou r c e : F o r be s , Se l f - Driving Cars Are Coming, 10/13/201432Auto Ownership

Googl e an d Uber c ompete to develop driverless taxis.Eliminating the “driver” sign ificantly reduces overhead costs.Consumers can expect a driverless U ber fleet by 2030.33 Source: Mob il i t y Lab , U be r’s P l a n f o r Se l f - Driving Cars Bigger Than It’s Taxi Disruption, 8/18 /2 015Sharing Economy

D i s r up ti o n to th e so-called “accident economies” Impacted Industries

35 5 / 201 2 A s of 2015, approximately 38% of P&C insurance i ndustry premium is derived from personal autom obile insuranceAccording to Celent, au to insurance premiums coul d drop 60% star ti ng i n 2020 ’ s IHS Automotive predicts w or l d w i de s e l f driving vehicles sales of 230,000 by 2025; 11.8m by 2035 So urce: SNL, P&C Net Premiums W r i tt e n , 3 / 28 / 2 0 1 6 , I H S Au t o m o t i v e , E m e r g i n g T e c hno l o g i e s : Au t ono m ou s C a rs - N o t If , Bu t W h e n , 1 / 2 / 20 14 , C e l en t , En d o f Au t o I n s u r a n c e , Insurance Industry Impact

“ Sel f-d ri v i n g cars and ride-sharing programs will completely disrupt the car-insura nce industry.”"Accident frequenc y will decline to where the differ en c e among d r i v i ng beha v i o r s be c omes neg li gible and it is difficult to charge a meani ngful premi um for insurance.""Insurance will take the form of commercial product liability instead of personal driver li ab il i ty as w e l et the r obots do the d r i v i ng." “T h i s c ou l d be the beg i nn i ng of the end for the c a r - i n s u r an c e bu si ne ss.” Bu si n ess I n si d er J u ly 21, 2015 36 Insurance Industry Impact

P o t en ti al s hifts in liab ilities and premiumsAuto liabilityP roduct recall Cyber risk Auto physi cal damageEquipment breakdown/w arr antyPr oducts liability Transitio n to f ull v eh i c l e au t ono my ------ V a r y i n g degrees of impact over timeLikely to shrinkLikely to increaseLikely to increase L i ke l y to i nc r ease L i ke l y to i nc r ease L i ke l y no m ate ri al chan g e 37

I n e v i t a b le 38ComplexEvolvingChallengi ngInsu rance of a uto exposure will change, possibly dramatically o v er time Liability shifts from driver to manufa c tu r e r s and te c hno l ogy c ompan i es Auto ph y s ical damage, cyber, products warranty may growExposures will be more complex – C yber/software, car manufacturer , d r i v er Coverage issues will emerge and take time to s tab ili z e Telematics use will grow – c ont i nue the p r og r e s s to w a r ds i nd i v i dual r atema k i ng Insurance industry should understand the issues; be prepared to adjust and innovate ( pa r t i c u l a r l y tho s e w i th mate r i al pe r s onal or c ommer c i al auto po r tfo li o s ) Looking Ahead

39 OEM’S HOSPITALS FIRS T RESPONDERS TELECOMMUNICATIONS URBA N PLANNERS MASS TRANSIT MANUFACTURERS AND OPERA TORS ELECTRIC GRID CONSUMER ELE C T R O N I CS PERCEPTIO N SYSTEMS MATERIAL S SCIENCE NO N - F O SS I L FUE L PROVIDERS COA L INDUSTRY OI L COMPANIES TOURIS M UNIONS CHIROPRACTIC TOR T & LIABILITY RELA T E D LE G A L F I ELD S TRANSPORTATION R E G U LA TO RS CRAS H TESTING FAC I L ITI E S DRIVE R EDUCATION TRUCKIN G AN D FREIGHT PARKIN G LOT O PERA TO R S VENTUR E CAPITAL VEHICLE F I N A NC I N G/ LEAS I NG MINING TRANSPORTATION M O N ITO R I N G BIK E MANUFACTURERS RESEARCH UN I VERS ITI ES ENGINEERING P R OG R A M S AUT O REPAI R FACILITIES TRAFFIC I N F R AS T RUC T URE PERSONA L AND C O MM E RC I A L I N S UR A NCE STOC K AN D BOND E X CH A N G E S TIR E INDUSTRY HUMA N MACHINE I N T E RFACEARTIFICIAL INTELLIGENCEMILITARY SOFTWARE DEVEL O PER S CLOU D COMPUTING MOBIL E DEVICE M A NU FA C TURERSVEHICLE SALVAGE OPERATIONSCUSTOMS AND BORDER PA TROLAUT O CLAI M LITIGATION VEHICL E SUPPLIERS ( TI E R 1 & 2 ) USE D VEHICLE DEALERSH I PS Other Impacted Industries

Presentation Overview Intelligent Transportation Systems Connected Vehicle Environment Autonomous Vehicle Environment Market Opportunity and Risks Cyber Security Road Confusion Research

Technical Challenges Governance Challenges 41 Cyber Security

T ech n ica l C h a llenges Cameras Radars Sensor Sensor Processing Sensor Fusion 3D Scanning Lidars Ultrasound sensors Sensor Processing Sensor Processing Action Engine V ehicle Controls Brake/acc Steering etc. V isualization/Display Sub-system Raw data Object parameters T ime stamp Dimensions Position/velocity 3D Map Actions Do nothing W arn Complement Control Compressed data comm. Sense Understand Act GPS IMS “Maps” Driver state

Cameras Radars Sensor Sensor Processing Sensor Fusion 3D Scanning Lidars Ultrasound sensors Sensor Processing Sensor Processing Action Engine V ehicle Controls Brake/acc Steering etc. V isualization/Display Sub-system Raw data Object parameters T ime stamp Dimensions Position/velocity 3D Map Actions - Do nothing - W arn - Complement - Control Compressed data comm. Sense Understand Act GPS IMS “Maps” a priori info Driver state O n -B oa rd Un it D a t a s t o r a g e 3 T ech n ica l C h a lle n g es

V e h icle Cameras Radars Sensor Sensor Processing Sensor Fusion 3D Scanning Lidars Ultrasound sensors Sensor Processing Sensor Processing Action Engine V ehicle Controls Brake/acc Steering etc. V isualization/Display Sub-system Raw data Object parameters T ime stamp Dimensions Position/velocity 3D Map Actions - Do nothing - W arn - Complement - Control Compressed data comm. Sense Understand Act GPS IMS “Maps” a priori info Driver state C l ou d s e r vi c es O n -B oa rd Un it D a t a s t o r a g e 3 T ech n ica l C h a lle n g es

V e h icle Cameras Radars Sensor Sensor Processing Sensor Fusion 3D Scanning Lidars Ultrasound sensors Sensor Processing Sensor Processing Action Engine V ehicle Controls Brake/acc Steering etc. V isualization/Display Sub-system Raw data Object parameters T ime stamp Dimensions Position/velocity 3D Map Actions - Do nothing - W arn - Complement - Control Compressed data comm. Sense Understand Act GPS IMS “Maps” a priori info Driver state O n -B oa rd Un it D a t a s t o r a g e I n f r a st r u ct u re 3 T ech n ica l C h a lle n g es C l ou d s e r vi c es

V e h icle Cameras Radars Sensor Sensor Processing Sensor Fusion 3D Scanning Lidars Ultrasound sensors Sensor Processing Sensor Processing Action Engine V ehicle Controls Brake/acc Steering etc. V isualization/Display Sub-system Raw data Object parameters T ime stamp Dimensions Position/velocity 3D Map Actions - Do nothing - W arn - Complement - Control C ost (mo n C o e mpr e y sse d ) data comm. Sense Understand Act GPS IMS “Maps” a priori info Driver state O n -B oa rd Un it D a t a s t o r a g e I n f r a st r u ct u re C o n s t ra i n t s 3 C omp u t a t ion o v er h ead S p a ce En e r g y T ech n ica l C h a lle n g es C l ou d s e r vi c es

V e h icle Cameras Radars Sensor Sensor Processing Sensor Fusion 3D Scanning Lidars Ultrasound sensors Sensor Processing Sensor Processing Action Engine V ehicle Controls Brake/acc Steering etc. V isualization/Display Sub-system Raw data Object parameters T ime stamp Dimensions Position/velocity 3D Map Actions - Do nothing - W arn - Complement - Control C ost (mo n C o e mpr e y sse d ) data comm. Sense Understand Act GPS IMS “Maps” a priori info Driver state O n -B oa rd Un it D a t a s t o r a g e C ha ll e n g e s H W secu r it y S W secu r it y O S secu r it y Net w o r k secu r it y P r iv a cy I n f r a st r u ct u re C o n s t rain t s 3 C omp u t a t ion o v er h ead S p a ce En e r g y T ech n ica l C h a lle n g es C l ou d s e r vi c es

G o v e r n a n ce C h a llengesEncourage security by design and security testingData ownership will affect privacy and securityForensics ability Role of road operators

Presentation Overview Intelligent Transportation Systems Connected Vehicle Environment Autonomous Vehicle Environment Market Opportunity and Risks Cyber Security Road Confusion Research

BROADENING USE “ The application of a TCD beyond the specific case or specific set of related cases for which it was intended.”

ERRATIC USE “ The application of TCDs with differing meanings in locations where one or a discrete set of TCDs should be consistently employed”

INCONSISTENCY IS EVERYWHERE OPTION LANE EXITS AND MULTI-LANE EXITS

F O U R G E OMET RIC DESIGN O PTIONS . . . F OUR SCENARIOS

Exiting Lane Location 1

Exiting Lane Location 2

Exiting Lane Location 3

Exiting Lane Location 4

L O C A T I O N 4 I N COMPARISON TO LOCATION 3

How does a machine make logical sense of this without extensive up-front data collection and rules for every interchange?

Where does the lane end?

Erratic use of guide sign arrows fails to provide differentiation

CONFLICTIN G AN D AMIBIGUOU S REGULATIONSREFUSAL TO INNOVATE OR CHANGEPOOR UNDERSTANDING OF HUMAN FACTORS PRINCPLES NO UNDERSTANDING OF THE CONSISTENCY PRINCIPLELAC K OF RESOURCES APATHY AND DEFEATISMPOOR EXAMPLES IN NEARBY AREASRoot Causes?

CONVERSATIO N BETWEE N HUMA N FACTORS ENGINEERS IN TRANS PORTATION AND MACHINE VISION AND LEAR NING ENGINEERS IMPROVED OVERSIGHT OF LOCAL AGENCIESDEVELOPMENT O F LEVEL OF READINESS CRITERIAEVALUATION PROGRAMS AND SERVICEABILITY AUDITS MACHIN E VISION LEARNING CLEARINGHOUSE COLLABORATION WITH POLICY DEVELOPMENT ANALYSTS• $$$$$$ Solutions

Presentation Overview Intelligent Transportation Systems Connected Vehicle Environment Autonomous Vehicle Environment Market Opportunity and Risks Cyber Security Road Confusion Research

Coordinated by the Intelligent Transportation Systems Joint Program Office (ITS JPO) Conducts and supports research with: Federal Highway Administration (FHWA) Federal Motor Carrier Safety Administration (FMCSA) Federal Transit Administration (FTA) National Highway Traffic Safety Administration (NHTSA) U.S. DOT Automated Vehicle Research Program

Program Research Tracks Enabling Technologies Digital Infrastructure Communications Technology Research Safety Assurance Transportation System Performance CACC, Speed Harmonization, and Platooning Lateral Control First/Last Mile and Transit Operations Testing and Evaluation Interoperability Testing Methods Benefits Assessment Policy and Planning Human Factors Cybersecurity Functional Safety and Electronics Reliability Electronic Control Systems Transportation Planning Stakeholder Engagement Federal Policy Analysis Standards

Automated Vehicle Policy Research Support policies that ensure safe, efficient and equitable integration of Automated Vehicles (AVs). Key Activities: Identify and conduct policy research activities Assess potential USDOT role in automation Coordinate with other Federal research initiatives/programs Engage external policy stakeholders for input

DRAFT Automation Policy Research Roadmap

TRB/AUVSI Automated Vehicle Symposium Policy Breakout Session U.S. DOT State Roundtable on Automated Vehicles Annual meetings and conferences TRB, ITS America, AAMVA, etc. ITS PCB Webinar Series Road Transport and Automation Automation and Planning Test deployments and pilots U.S. DOT Stakeholder Engagement

Related Efforts in AV Policy AAMVA Autonomous Vehicle Information Sharing Group AAMVA Autonomous Vehicle Best Practices Working Group NCHRP Roadmap for AASHTO: 20-24(98), 20-102 AASHTO Public Policy Workshops Uniform Law Commission National Council of State Legislatures And others…..

Questions / Follow-up Betsey Tramonte U.S. DOT / FHWA betsey.tramonte@dot.gov John M Broemmelsiek, PE U.S. DOT / FHWA john.broemmelsiek@dot.gov