r&d gap and trend analysis for utonomous and connected
TRANSCRIPT
S Y N T H E S I S P A R T N E R S , L L C
R&DGapandTrendAnalysisforAutonomousandConnectedVehicles:OnConnectivity,SensorsandSensorSystems
11250RogerBaconDrive,Suite2Reston,VA20190,USA
www.synthesispartners.com
SynthesisPartners,LLC©
R&D Gap and Trend Analysis for Autonomous and Connected Vehicles:
On Connectivity, Sensors and Sensor Systems
Prepared for the US Department of Energy,
Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office.
Publication Date: November 2017
Collection cut-off date: August 8, 2017
Contract No. DE-DT0006388
Copyright © 2017 by Synthesis Partners, LLC
ApprovedforPublicRelease
PleasecontactMr.StevenBoyd,VehicleTechnologiesOffice,USDepartmentofEnergyat([email protected]),orMr.ChrisWhaling,SynthesisPartners,LLCat
([email protected]),withquestionsorcomments.
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Acknowledgements
TheDepartmentofEnergy,OfficeofEnergyEfficiencyandRenewableEnergy,VehicleTechnologiesOfficeprovidedfundingforthisworkundercontractnumberDE-DT0006388.SpecialthankstoMr.StevenBoyd,TechnologyDevelopmentManager,forhistechnicalguidanceandmanagementofthiseffort,andtotheVehicleTechnologiesOfficeandtheNationalEnergyTechnologyLaboratory(NETL)fortheirongoingoversightandsupportofthiseffort.SynthesisPartnersAuthors:
• ChristopherWhaling,P.I.• RichardHolcomb,Manager• SteveJohnson,SeniorResearcher• FriedaHanratty,Ph.D.,SeniorAnalystandLinguist
Review:TheinformationinthisreporthasbeendevelopedbasedonanongoingreviewbySynthesisPartnersofdatafromhundredsofanonymized,primaryindustrysourcesoveranumberofyears,alongsidethousandsofsecondarysourcesin2017alone.Eachprimarysourceinparticularisherebyacknowledgedandthankedfortheirtimeandcontributionstothiseffort.AllerrorsandomissionsofcourseremainthesoleresponsibilityofSynthesisPartners.TolearnmoreaboutpublicinformationthatisavailableaboutthesourcesandmethodsemployedbySynthesisPartnersinperformingthisandrelatedresearchfortheUSDepartmentofEnergy,OfficeofEnergyEfficiencyandRenewableEnergy,VehicleTechnologiesOffice(VTO),pleasecontactMr.ChrisWhaling,[email protected].
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LegalDisclaimer
ThisreportisbasedonworksponsoredbytheUSDepartmentofEnergy.NeithertheUnitedStatesGovernmentnoranyagencythereof,noranyoftheiremployees,makesanywarranty,expressorimplied,orassumesanylegalliabilityorresponsibilityfortheaccuracy,completeness,orusefulnessofanyinformation,apparatus,product,orprocessdisclosed,orrepresentsthatitsusewouldnotinfringeprivatelyownedrights.Referencehereintoanyspecificcommercialproduct,process,orservicebytradename,trademark,manufacturer,orotherwise,doesnotnecessarilyconstituteorimplyitsendorsement,recommendation,orfavoringbytheUnitedStatesGovernmentoranyagencythereof.TheviewsandopinionsofauthorsexpressedhereindonotnecessarilystateorreflectthoseoftheUnitedStatesGovernmentoranyagencythereof.
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TableofContents
PageNo.1.0Introduction ……………………………….…………..………………………………………………62.0OverviewofFY16andFY17Tasking ……...……………………………………………………63.0KeyFindingsandRecommendations ………………...…………………………………………8
• ResultsfromFY16Work ..................................................................................................8
• ResultsfromFY17Work …………………………………………………………………….12
• Drill-Downon37R&DGapsIdentifiedinFY17 .....................................................13
• GapScoringMethodology …………………………………………………………………….14
• DistributionofGapsIdentifiedinFY17,byCategory……………………..………..15
• RankingofGapsIdentifiedinFY17,byCategory………………..…..………..….…..16
• SummaryDiscussionandAnalysis ……...………………..………………………..………..174.0Recommendations …………………………………………………………..……………..…....215.0Conclusion ……………………………………………………………………..……………..…....246.0SelectedBibliography ……………………………………………………………………………….257.0Appendix:ListofR&DGapsIdentifiedinFY17,inAlphabeticalOrder………….43
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ListofFigures,Tables,ChartsandDiagrams
PageNo.Figure1:StatisticsonSourcesAccessedinFY17 ……………………………………...7Table1:Top10WorldwideSemiconductorManufacturingEquipmentVendors,ByRevenue($Billions) ……………………………………………………………………10Figure2:LevelsofHardwareorSoftware,andInterdisciplinaryRDT&E(Research,Development,TestingandEngineering)Work,byCategory ………………................14Figure3:DistributionofGapsIdentifiedinFY17,byCategory ………………………….15Chart1:VehicleSensorsandIntelligenceMaterialsGaps ......................................16Chart2:V2XCommunicationsandIntelligenceNetworkingGaps .......................18Chart3:OtherR&DCollaborationGaps ..................................................................................20
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IntroductionThisreportprovidesanupdateonworkcompletedbySynthesisPartners,LLC(“Synthesis”)fortheDepartmentofEnergy’sVehicleTechnologiesOffice(VTO)undercontractnumberDE-DT0006388,duringfiscalyears(FY)2016and2017.SynthesisperformstargetedresearchtohelpinformVTOresearchanddevelopment(R&D)decision-makingaboutcriticaltechnologybottlenecks,gapsorconstraintsintheUSindustrialbaseandsupplychains.Thisreportisthepublicoutcomeofaglobal,data-drivenapproach–employinghundredsofprimarysourcesandthousandsofsecondarysources–toassessandcharacterizepotentialR&DgaptopicareasthatarerelevanttotheVTO’smissionofenergyaffordability,efficiencyandresiliency,andthatcanprovideapathtowardtransitioningVTOR&DworkintoUS-basedjobs.ThisreviewcoverstargetedresearchonR&Dgapsregardingtechnologiesandcapabilitiesinvehicleelectrictractiondrive,autonomousandconnectedvehiclesystemsandsectors.TwoseparateinquirieswerepursuedinFY16andFY17.ResearchduringFY2016focusedonidentificationandprioritizationofresearchanddevelopment(R&D)fieldsintheautomotivesectorthathaveastrongchancetogrowintheUSAbecauseofuniqueUS-basedcapabilitiesandstrengths.ResearchworkduringFY2017focusedonidentificationandprioritizationofgapsinR&Dthatpointtopromising,hardware-orientedresearchdirectionsinautonomousandconnectedvehiclefields.PrimarysourcesandEnglish-language(andtargetedforeignlanguage)secondarysources,aswellasproprietaryandcommercialdatabases,wereaccessedduringtheperiodofthiswork.AhighpriorityhasbeenplacedonR&Dfieldsthatareamongthehighest-value(basedonshareofUSexportsandrevenuegrowthrates),andthatarerelevantoverfive-plusyearplanninghorizonsforVTOdecision-makersseekingsignificantenergyefficiencyandcoretechnologycostreductions.ThegapintelligenceworkinFY16andFY17isultimatelyintendedtoidentifyhighpotentialtargetsforUSautonomousvehicleR&Ddevelopmentandhigh-valuejobcreation.OverviewofFY16andFY17TaskingDetailsonthetaskinganddeliverablesproducedovertheFY16andFY17periodareavailablefromVTO.Thisreportprovidesasummaryreviewofthepublickeyfindingsandrecommendationsproduced.DuringFY16SynthesisassessedUSexportsectorsrelevanttoelectricalengineeringintheautomotiveindustry,intermsofrelativesize(bysales)andgrowthrates,inordertoidentifyhighstrengthorcompetitiveUSsectors.Thetop-rankedsectorsidentifiedarehometosomeofthemosthighlycompetitiveandinnovativeUS-basedfirms.ThesesectorswerethenreviewedtoidentifyandassesspotentialnewR&Dareas,definedastechnicalareasinwhichtheVTOhasnotpreviouslyengagedand
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whereR&Dinvestmentscouldaddressagap.SuchnewR&Dareasrepresentacomplexsetoftechnicalneedsthataregenerallybeyondtheabilityofanyindividualcompanytoaddress,andwhichareincludedunderSynthesis’interpretationoftheVTOmission.DuringFY17,Synthesisidentified,characterizedandprioritizedkeygapsincurrentresearchactivities,inordertohighlightpromisingR&Dpossibilitiesinthefieldsof:
• LightDetectionandRanging(LiDAR);• Sensorsystemdevelopment;and• Vehicle-to-vehicle(V2V)orvehicle-to-anywhereconnectivity(V2X).
PromisingresearchintheFY17workisdefinedasR&Dactivitiespertainingtoagapinautonomousandconnectedvehicletechnologythatisalso:
• Hardware-focused;• US-based,orhasthepotentialtobeUS-based;• Couldreachcommercialvehiclemarketsin5-10years;and• Hasthecapabilitytoreducecosts,ideallybyasignificant(>50%)amount.
Figure1providesasummaryreviewofsourcesaccessedinFY17alone.AsimilarlevelofeffortwasexecutedinFY16.
Figure1:StatisticsonSourcesAccessedinFY17.
Sources Quantities
SourcesChecked(February2017–July2017)Wellover1,000websites,newsarticles,pressreleasesandresearchpaperswereinitiallyreviewedtoidentifythemostappropriateinformationsources.Fromthisinitialsearch,alongwithareviewofSynthesis’internalproprietarydatabaseofnearly1,000primarysources,approximately444highlyrelevantcompanies,associations,laboratoriesorindividualswereidentifiedforcloserexamination.
Thousandsofsourceswerereviewedtoidentify444highlyrelevantcompaniesandindividualsforthistask.
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High-RelevanceContactsFromamongthe444high-relevanceorganizationsorindividualsidentified,374contactsweremadeviatelephonecallsandemailstodetermineavailabilityandinterest.
374contactsmadeviatelephoneandemail.Fromthe374contacts,73in-depthinterviewswerecompleted.
DistributionofHigh-RelevanceContacts:
Distributionofhigh-relevancecontacts:
• OEMs=6%• Tier1=25%• Tier2=26%• Tier3=7%• RD&E=7%• Academic=2%• Association=6%• Consultant=4%• Investor=3%• Software=4%• FederalGovernment=5%• StateGovernment=3%• LocalGovernment=2%
Source:SynthesisPartners,LLC(2017).KeyFindingsandRecommendationsThefollowingarepriorityrecommendationsaboutR&DgaptargetsidentifiedduringtheFY16andFY17researchperiod.TheserecommendationsarebasedsolelyonSynthesis’conclusionsanddonotreflecttheviewpointsoftheVTOoranyparticularsource.AnonymityofprimarysourcesismaintainedunlessSynthesisreceivesspecificapprovaltosharethisinformationbyindividualsources.ResultsfromFY16WorkSynthesisrankedtentop-levelR&Dtopicsraisedbyprimarysources(basedonopen-ended,in-depthinterviews),intendedtorepresentabroadrangeofinterestsandtechnicalfields,basedonfourvariablesusingabasicscoringsystem.Thefourvariablesusedforrankingarea)expectedenergyefficiencyimprovement;b)amountofscientificevidenceprovidedbysources;c)relevancetofast-growingUSindustrysectors;andd)aGoogleScholaractivityindex.
OEMs6%
Tier125%
Tier226%Tier3
7%
Academic2%
Associa7on6%
Consultant4%
Investor3%
SoAware4%
FederalGovernment5%
StateGovernment3%
LocalGovernment2%
RD&E7%
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Thetop-tenR&DtopicsthatwereidentifiedfromSynthesis’contactwith100sofsourcesare:
1. Embeddedsoftware2. Variablecompressionratio3. Motors4. Powerelectronics5. Carsharing6. Autonomousvehicles7. Vehicleembeddedsoftware8. Solid-statebatteries9. Carbonfiber10. Advancedcylinderdeactivation
Intermsofhighest-priorityrecommendations,SynthesisdrilleddowntoidentifythefollowingthreeareasasbothdirectlyrelevanttofutureautomotivetechnologycapabilitiesandamongexportcategoriesinwhichtheUSshowsunique,industrialstrength:
• #1:Automotivesemiconductormanufacturingandmanufacturingequipment.Thisincludessemiconductorsdesignedfordiscreteandintegratedcircuits(ICs)forpowermanagement,electrictractiondrivepowerelectronics(invertersandconverters),aswellassignalsprocessingforadvanceddriverassistancesystems(ADAS),andinfotainmentsystems.
• #2:Embeddedsoftware.Thiscoverssoftwarewrittentoexecuteaparticularfunctioninaparticularhardwareimplementation,suchasfield-programmablegatearrays(FPGAs),toincludefunctionalcapabilitiesthatareincreasinglyimportantfor“smart”vehiclesandautomotiveelectronics,suchas:
o Machinevision;o Imageanalysis;o Signalsprocessingforself-drivingcars;o Safetymanagement;o Wirelessupdating;o Artificialintelligence;o Automotivecloudengineering;ando Softwaretodriveawiderangeoffield-programmablegatearrays
(FPGAs)thatcanbealteredbasedontheapplication.
• #3:Integrationofpowermanagementandwirelessbasebandinapplicationprocessors.Thiscoverssystem-on-chip(SoC)design,inwhichanICintegratesmultiplecomponentsofacomputerorotherelectronicsystemintoasinglechip.Itmaycontaindigital,analog,mixed-signal,andoftenradio-frequencyfunctions–allonasinglechipsubstrate.SoCsareverycommoninthemobileelectronicsmarketbecauseoftheirlowpowerconsumption.Theyarebecomingthefirstchoiceforfutureembedded
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systemdevelopments–whichareofincreasingrelevanceinautomotiveapplications–duetotheincreasingdemandforhigherperformance,reliabilityandpowerdensityinautonomousapplications.
Itisnotablethathybridandelectricvehiclemanufacturingaredrivingvehiclestowardcomputerization.Microcontrollers,sensorsandanalogdeviceshaveledthegrowthofautomotivesemiconductors.McKinseyanalystshavenotedthatthreeareaswilldrivethenextwaveofgrowth:1)furtherelectrificationofdrivetrain;2)‘consumerization’ofautoelectronics,and3)vehicleintelligence(includingactivesafetyinnovationsandconnectivity-enhanceddriving).Theelectrificationofthedrivetrainduetohybridorfullyelectricvehiclesmayleadtothe“largestexpansionoverthenexttenyears,”accordingtotheseanalysts.Thedrivetrainaccountsfor30%ofallsemiconductorcontentinanautomobile,and“developingalessexpensivealternativetoIGBTswouldbeonewaytowinthemarketshareinthisarea.”1Automotivepowermanagementisthethirdlargestmarketforsemiconductors,comprisingabout8%ofthetotalmarketin2015,witha10.8%growthratecomparedto5.5%forthetotalICmarket.In2014,theautomotivemarketforICstotaledabout$21.7billion.Amongstautomotivepowermanagementapplications,demandisstrongestinadvanceddriverassistancesystems(ADAS),whichincludeLaneDepartureWarning(LDW),ForwardCollisionWarning(FCW),AutomotiveEmergencyBraking(AEB);andinfotainmentsystems.Fromamarketperspective,theworldwideautomotivesemiconductorsindustryismorethana$24billionbusinessandhasexperiencedoneofthefastestgrowthratesofanylargesegmentinthe$300billionworldwidechipmarket-averaging8percentannuallybetween2002and2012.Thetop-rankedcompaniesinvolvedinsemiconductorequipmentmanufacturingareprovidedinthetablebelow.USheadquarteredfirmsinthissectoraccountedfor39.5%oftheglobalmarketin2015.ThisdemonstratesanapparentcompetitivestrengthofUSfirms,fromwhichSynthesisisworkingtoidentifynewR&DtopicsrelevanttotheVTOmission,withtransitionopportunities.
Table1:Top10WorldwideSemiconductorManufacturingEquipmentVendors,ByRevenue($Billions)
2015Rank
2014Rank
CompanyName HQLocation
2015Revenue
2015Market
2014Revenue
2014-2015Growth(%)
1“OnSemiconductors,”byHaraldBauer,MarkPatel,NickSanthanam,FlorianWeigandBillWiseman,McKinsey(2013);and“WinningShareinAutomotiveSemiconductors,”byDougParkerandChristopherThomas,McKinsey(2013).
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Share(%)
1 1AppliedMaterials
SantaClara,CA,USA
$6.42billion 19.1
$6.34billion 1.3
2 4LamResearch
Fremont,CA,USA
$4.81billion 14.3
$3.86billion 24.7
3 2ASML
Veldhoven,TheNetherlands
$4.73billion 14.1
$5.64billion -16.0
4 3TokyoElectron
Tokyo,Japan $4.33billion 12.9
$4.67billion -7.3
5 5KLA-Tencor
Manassas,VA,USA
$2.04billion 6.1
$2.13billion -4.0
6 6 ScreenSemiconductorSolutions
Kyoto,Japan $0.972billion 2.9
$1.13billion -13.9
7 7 HitachiHigh-Technologies
Tokyo,Japan $0.788billion 2.3
$0.937billion -15.9
8 8Nikon
Tokyo,Japan $0.724billion 2.2
$0.818billion -11.5
9 9HitachiKokusai
Tokyo,Japan $0.634billion 1.9
$0.599billion 5.7
10 10ASMInternational
Almere,TheNetherlands
$0.583billion 1.7
$0.557billion 4.5
Others $7.58
billion 22.5$7.27billion 4.2
TotalMarket**Numbersmaynotadd
tototalsduetorounding.
$33.60billion 100
$33.934billion -1.0
Source:SynthesisPartners,LLC(2016),andGartner,“GartnerSaysWorldwideSemiconductorWafer-LevelManufacturingEquipmentMarketDeclined1Percentin2015”,04-06-16,http://www.gartner.com/newsroom/id/3276017,accessed07-07-16.Intheautomotivesemiconductorsectortherearecertaintechnicalgapsthat–iffilledbyUS-basedengineeringR&Dadvances–presentaconcreteopportunityto
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expandUS-basedadvantagesindesignanddevelopmentofcriticalengineeringsystemsandproductsfortheautomotiveindustryandbeyond.US-headquarteredfirmsareamongthetop-rankedcompaniesintheautomotivesemiconductorsectorandaccountedfor39.5%oftheglobalmarketin2015.Forthisreason,researchworkincoreR&Dinthissector,includingresearchonnext-generationenergyefficiencytargetsforautomotiveapplications,hasinherentlystrongtransitionopportunities.Withinthesemiconductorspace,SynthesisassessedinFY16thatthefollowingtechnicalandengineeringfieldsarenotableforfurtherdiscussionregardingR&Dgaps.
• Functionalchip-designsforautomotivemanufacturability.Forexample,Jen-HsunHuang,[then]chiefexecutiveofgraphicalchipmakerNvidiaCorp.,said[in2016]thatsomeofits’processorswerebeingstrainedasTeslahasincreaseditscars’capabilities.
• Instruments,methodsanddesignsthatensurechip-designcomplexitykeeps
pacewith,ratherthanoutpaces,manufacturingproductivity.Putanotherway,thepresentchallengeisthatchip-designadvancesareoutpacingmanufacturabilityadvances,andthus,forexample,lithographypresentlyappearstolagthegrowingneedforcomplexdesignsexecutedonsmallersurfaceareasatlowercosts(seeSynthesis’FY16extremeultraviolet(EUV)casestudy).Oneincreasinglycandesignchipsthatareneeded,butcannotbeproducedatacompetitivecost–andthispresentskeyresearch,development,engineeringandtestinggaps.
• Thereisacontinuousneedfordevelopmentalworkinmulticoresystem-on-
a-chip(SOC)architectures,toenablefasterand“smarter,”increasinglyfunctional,smaller,morepowerdenseelectronics.
ResultsfromFY17WorkAtthebeginningofFY17,VTOtaskedSynthesistofurtherframetheopportunityforfutureR&DtargetsbasedonfindingsfromFY16,andtofocusinparticularontheareasofautonomousdrive,LiDAR(lightdetectionandranging)sensorsandconnectedvehicles.Synthesisidentified37R&DgapsinFY17,fromtheintegratedprimaryandsecondarysourceanalysis,fromFebruarythroughAugust2017.Synthesisassessednumeroustrendsandtechnologiesrelatedtoautonomousdrive,sensorsandconnectedvehicles.Findingsarederivedfromastructured,data-drivenprocessintendedtoproducefindingsonplausible,high-valueR&Dtargetsforsustainable,US-basedjobs.EachR&Dgapcanbetracedbacktoprimarysourceinterviewsorsecondarysourcedocuments.Agapisbasedonspecificneedsstatedbykeysources,“inongoing
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commercialR&Dandproductdevelopmentactivities,”whichpertaintoautonomousandconnectedvehiclesthatis:
• Hardware-focused;• US-based,orhavethepotentialtobeUS-based;• Couldreachcommercialvehiclemarketsin5-10years;and• Hasthecapabilitytoreducecosts,ideallybyasignificant(>50%)amount.
The37R&Dgapsarecategorizedandscored,withtrace-backcapabilityforeachgapandscoretoSynthesis’internaldatasets.Thequantitativeanalysisofthegaptopicsisofferedasaninitialviewpoint.Synthesiswelcomesdiscussiononadditionalperspectivestoassessthenatureofthegapintelligenceobtained.Thefollowingsummarizesthefindings,assessesthedistributionofgapsacrosscategories,andprovidesananalysisofthegapsbasedonquantitativescores.Drill-Downon37R&DGapsIdentifiedinFY17The37R&DgapsidentifiedinFY17aregroupedintothreecategoriesdevelopedbySynthesistoprovideguidanceontrendsinfindings,asfollows:
• VehicleSensorsandIntelligenceMaterials:Hardwareforlow-cost,high-performance,energydenseon-vehicledatastorage,processingandcommunicationonthevehicle;includesLiDAR.
• Vehicle-to-Vehicle(V2X)CommunicationsandIntelligenceNetworking:Hardwareforlow-cost,high-performance,energydense,secureandreliablecommunicationsV2X,includingsensorsandsensorfusion.
• OtherR&DCollaborationOpportunities:OtheropportunitiestoaddressR&DgapsintheautonomousvehicleandV2X
ThethreebinsillustratetherangeofR&DtopicsthatSynthesisidentifiedasgapsduringthisresearch.Thesecategoriesarenotmutuallyexclusiveandarecertainlynottheonlycategoriesthatcouldbedevelopedfromthegaps.Follow-onanalysesthatassesstherelationshipswithinandbetweensmallergroupsofgapsforthepurposeofidentifying(moreefficient,multi-impactR&Dtopicsinmoredetailcanbedone.Asjustoneexampleofsuchadrill-downongapcategories,andfocusingonlyatahighlevelbasedonnumerousconversationswithsources,thereisanapparentrelationshipbetweentheindividualgapcategoriesusedinthisreportandthelevelofinterdisciplinaryRDT&E(research,development,testingandevaluation)workthatisneededtorespondtoeachgap.ThisviewisreflectedintheFigure2below.Asresearchmovesawayfromaddressinggapsin(e.g.)individualsensorsorsensormaterials,andtoward(e.g.)V2Xcommunicationssystemsandcomplexsystems,theworkrequired–speakingingeneral,atahigh-level–isincreasinglyofamulti-disciplinarynature,movingbeyondhardwarealone.Moreresearchonthevalidityandimplicationsofthisfindingisrecommended.
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Figure2:LevelsofHardwareorSoftware,andInterdisciplinaryRDT&E(Research,Development,TestingandEvaluation)Work,byCategory
GapCategoryHardwarevs.SoftwareWork
IndicatedbySources
LevelofInterdisciplinaryRDT&EWorkIndicatedby
Sources
VehicleSensorsandIntelligenceMaterials
MostlyHardware LOW
V2XCommunicationsandIntelligenceSystems
IntegratedHardwareandSoftware
MEDIUM
OtherR&DCollaborationTopics
Systems-BasedSoftware-DrivenWork,AppliedtoComplexNeeds
HIGH
Source:SynthesisPartners,LLC(2017)GapScoringMethodologyAscoringsystemwasdevelopedtoanalyzetherelevanceofthe37gapsidentifiedinFY17andtointroduceinitialrankingstoaddressthework-planobjectives.Additionalscoringtechniquesarefeasibleandavailablefordiscussion(e.g.,scoresthatapplytoclustersofgapsorspecifictechnologyattributesofgaps).Tostartthediscussion,thefollowingthreescoresareemployed:
• Score#1:Work-planrelevanceForeachattribute,whichincluded(1)hardware-focus,(2)morethanfive-yearrelevance,and(3)privatesectorR&Dinvestmentgaps,thefollowingscoreswereapplied:
o 0pointsassessediftheattributedoesnotapply;o 5pointsassessediftheattributepartiallyapplies;ando 10pointsassessediftheattributefullyapplies.
Themaximumscoreonwork-planrelevanceis30points.
• Score#2:Numberofsourcesinagreement
o 1pointforeachcompanyorindividualexpertsourceinagreement;o 25pointsifthegapisdevelopedfromanAVS2017consensusfinding,
(approximatesthe25experts,conservativelyspeaking,holdingtheconsensusview).
Theminimumscoreisoneandthemaximumisinincrementsabove25.
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• Score#3:SumtotalSummationofScores#1and#2aboveisusedastheintegratedSynthesisfinalscoreforfinalrankings.(Again,thisisjustonewayofrankingthegapsandadditionalscoringapproachesareavailablefordiscussion.)
DistributionofGapsIdentifiedinFY17,byCategoryThe37gapswereanalyzedandplacedinoneofthreecategories,asdepictedinFigure3.Ofcourse,differentcategoriescouldbedevelopeddependingonhowdetailedoneisinassessingeachgap,orgroupofgaps.Figure3:DistributionofGapsIdentifiedinFY17,byCategory
GapCategory
ProportionofallGaps
VehicleSensorsand
IntelligenceMaterials
30%
V2X
CommunicationsandIntelligenceNetworking
30%
OtherR&DCollaborationOpportunities
40%
Source:SynthesisPartners,LLC(2017).
IntegratingthefindingsfromFigures2and3above,weseethatapproximately70%ofthegapsdonotinvolveahardware-drivenapproach.Thisisnottosuggestthat70%ofgapshavenohardwarecomponent,ratherthatthereisarelativelylowerlevelofhardwarefocusinferredinaverylargepercentageofgaps.Further,thisfinding–andtheunderlyingdatawhichSynthesisassesseswillcontinuetoreflectarelativelysignificantincreaseinmulti-disciplinaryR&Dneeds–suggestsatrendawayfromhardwareonlytopicsandthegrowingsignificanceofR&Dprojectsthataddressthecost,quality,efficiencyandeffectivenessofinterdisciplinary,software-enabledmobilitysystems.
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RankingofGapsIdentifiedinFY17,ByCategorySynthesisanalystsassignedscorestoeachgapbasedusingthemethodologydiscussedaboveusingthedatadevelopedduringthecourseofFY17.The37gapsareshownwithrankedscoresinCharts1through3below,followedbyasummarydiscussionandanalysis.AdditionalinformationontheoriginandnatureofthedataregardingeachofthegapsisavailablefordiscussionwithVTO.Chart1:VehicleSensorsandIntelligenceMaterialsGaps
0 10 20 30 40 50 60 70
MemorySupplyChainBottleneck
Memory-ComputeTechnology
PhotonicsforMemory,ComputeandDataTransfer
OpticalMaterialsDevelopment(incl.accesstoREs)
IndiumPhosphide6"SubstrateDevelopment
MiniaturizedProcessors
SensorFusion
OnboardScalableLow-CostPowerandProcessing(toEnableMachine
LearningOn-Vehicle)
SensorsforComplexDrivingEnvironments
Scalable,Low-CostAVBlackBoxes
SensorCostReduction
SumTotalScore
Work-PlanRelevanceScore
NumberofSources
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Source:SynthesisPartners,LLC(2017).SummaryDiscussionandAnalysisFindingsfromtheVehicleSensorsandIntelligenceMaterialscategory:
• Allgapsinthiscategoryhavehighwork-planrelevancescores;whichistosayatleast20outofamaximum30.Thisreflectstherelativesignificanceoftwoormoreoftheattributesofprivate-sectorinvestmentgap,5-yeartimehorizonsandhardwareR&Damongtopicsinthisgapcategory.
• Thesegapsindicateinparticularnumerouspathwaystohardware-focused
R&Dacrossthefullspectrumofthesensorandsensorapplication-engineeringdevelopmentchain.
• 30%ofthetotalgapsdiscovered(11of37)relatetovehiclesensorsandintelligencematerials.
• 46%(5ofthe11gaps)ofgapsinthiscategoryareassessedbySynthesisasconsensusgaps,indicatingthatspeakersattheAVS2017plenarysummarysessionsaddressedthesetopicsasgapsintheirconcludingslidesandaccompanyingdiscussions,perSynthesisanalystattendanceatAVS2017inSanFrancisco.Thesegapsappeartohavethelargestnumberofexpertsinagreement,basedonthenon-statistical,non-survey-based,primarysourceresearchinthisstudy.
• Thefollowingconsensusgapsthatwouldbenefitfromimmediateattention:o SensorCostReduction;o ScalableLow-CostAutonomousVehicle(AV)BlackBoxes;o SensorsforComplexDrivingEnvironments(e.g.,snow,fog,rain,sleet,
etc.);o OnboardScalableLow-CostPowerandProcessing(toenablesensor
operations);ando SensorFusion.
• Thesixremaininggapsinthiscategory–fromminiaturizedprocessorsto
memorysupplychainbottleneck–arenotconsideredconsensusgapsatthistime.Nonetheless,theydeservecarefulconsiderationbecause,fromamonghundredsofsourcescontacted,primarysourcestooktimetorecommendthegaptoSynthesisinanin-depthinterview.
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Chart2:V2XCommunicationsandIntelligenceNetworkingGaps
Source:SynthesisPartners,LLC(2017).
0 10 20 30 40 50 60 70
CatalyzeOEMtoBeSpectrumProviders
AVMobilityCommandCenters(e.g.,ATCCenters)
Over-the-airAVSoftwareUpgrades:Testing&Validation
HDMaps:Development,ValidationandStandardization
V2XInteroperability&Standards
OpenSourceCodeAVArchitectures
StrategicPlanning:SmartCars,SmartCities,SmartGrid
LowCostV2XConnectivitiy
R&DonLow-CostGeo-LocalizationWith2-3cmAccuracy
CyberSecurity:SecureandReliableV2XConnectivity
V2XSoftware-HardwareIntegration
SumTotalScore
Work-PlanRelevanceScore
NumberofSources
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SummaryDiscussionandAnalysisFindingsfromtheV2XCommunicationsandIntelligenceNetworkingcategory:
• Allbutoneofthegapsinthiscategoryhavehighwork-planrelevancescores,indicatingagainthesignificanceofboth5-yeartimehorizonsand/orhardwareR&Damongtheinterdisciplinarytopicsinthisgapcategory.
• 30%ofthetotalnumberofgapsidentified(11of37gaps)isclassifiedasV2Xcommunicationsandintelligencesystemsgaps.
• 82%(9of11gaps)ofthegapsinthiscategoryareidentifiedbySPasconsensusgaps,demonstratingthestronginterest-levelindicatedinprimarysources(again,non-statistical,non-survey-basedandasnap-shotintime)forthesegaps.
• Thetopfiveconsensusgapsinthiscategoryarehighlightedforrelevancetoourresearchwork-plan:
o V2Xsoftware-hardwareintegration(allaspectsofdesign,test,engineeringandpilotingofsensingandcommunicationprotocols);
o CyberSecurity(coversallaspectsofmakingV2Xcommunicationssecure);
o Low-CostGeo-Localizationat2-3cmaccuracy(requiresmanysensors,infrastructureandsystemstoworkseamlesslytogether);
o LowCostV2XConnectivity(requiresbenchmarkingtruecostsofpilotsystems,andpathstocostreduction);and
o StrategicPlanning(guidanceforfed-state-localgovernmentsandindustryindefiningneedsandopportunitiesforintegratedSmartCity,SmartGridandSmartAVCarapplications).
• Onlytwogapsinthiscategoryarenotconsideredconsensusgapsatthis
time.o AutonomousVehicleMobilityCommandCenters;o OEMsasSpectrumProviders,wheretheprimarysourcesexpresseda
needtoaddressthevolumeofdatathatvehicleswillbeproducingandusinginautonomousvehicleandconnectedvehiclescenarios.
• MostV2XandIntelligenceNetworkinggapshavebothstrongsupportinthe
AVScommunityandrelativelyhighwork-planrelevancescores.Thissuggestsopportunitiestotakeactiononmanygapsinthiscategory.
• SynthesisassessesthatVTOhasopportunitytoexpandengagementintonew
technicalfieldsbecauseallV2XandIntelligenceNetworkinggapsexistattheinterfacebetweensoftwareandhardware.
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Chart3:OtherR&DCollaborationGaps
Source:SynthesisPartners,LLC(2017).
0 20 40 60
BalancingVehicleDesignTimewithSoftwareUpgradeTime-Cycles
TechnologyTutorials(Deepdives,withAVrequirementsdexined)
Accessto/TransparencyofDatafromAVFleetManagers
CollaborativeConnectedVehicleTest-Beds
ProtocolsforModeTransfer(HumantoMachineControl)
3rdPartyDevelopment,TestingandValidationofAVEdgeScenarios
CollaborationonLessonsLearnedfromReal-WorldAVImplementation
ModelingandSimulationofAVCoding/SoftwareArchitectures:Guidelines,TestingandValidation
Dexiningthe"DrivingTask"Model:ForHuman-to-MachineTransfer
R&DonPedestrianAVandVRU,PedestrianInteractions
NeedforOpto-ElectronicEngineers(Incl.forLiDAR,Optics,SensorDev
Engineering)
SystematicAssessmentofAVTechnologyGaps
SituationalAwareness:DataRepositoryonTechUsed/Neededfor
AVs,SmartCity&SmartGrid
ScalabilityofAVEngineering(Hardware,Software,Multi-Context,
GlobalImplementations)
FundamentalCompetitionatCoreTechnologyLevel(e.g.,Robotics,MachineVision,AVSensor
SumTotalScore
Work-PlanRelevanceScore
NumberofSources
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SummaryDiscussionandAnalysisFindingsfromtheOtherR&DCollaborationgapscategory:
• Thetopgapsinthiscategoryhaverelativelyhighwork-planrelevancescores,showingthesignificanceoftwoormoreofprivatesectorinvestmentgaps,5-yeartimehorizonsandpossibilitiesinhardwareR&Damongthehighlyinterdisciplinarytopicsinthisgapcategory.
• 40%ofthetotalnumberofgapsdiscovered(15of37gaps)arebinnedinthis“Other”category–suggestingtheincreasingrelevanceofinterdisciplinaryresearchinautonomousandconnectedvehiclesforfutureR&Dplanning.
• 93%(14of15)ofgapsinthiscategoryareidentifiedbySPasconsensusgaps,demonstratingthestronginterest-levelindicatedinprimarysourcesinthesegaps.
o ItisplausiblethatthiscategorywillincreaseinsizeandsignificanceinthefutureandthereforeVTOhasaclearopportunitytoexploreexpandingitsroleinthetypesoftopicsraisedhere.
• Thetopicsraisedinthiscategoryaremostlymulti-disciplinary,underscoringapotentialroleforVTO.AmoredetailedreviewofthetopfiveconsensusgapsforthiscategoryiscontainedintheRecommendationssection.
• Onlyonegapisnotaconsensusgap–BalancingVehicleDesignTimewithSoftwareUpgradeTime-Cycles–andthereforeisrankedatthebottomofthiscategory.
RecommendationsThefollowingsummarizesrecommendationsfromSynthesis’FY17work.Theserecommendationsfocusonautonomousvehicles,connectedvehicles(vehicle-to-anywhere(V2X)),andLiDARsensors.Theyprovidethe“consensus”viewpoints,basedonSynthesis’rankingofinformationcollectedfrom100sofprimarysourcesand1,000sofsecondarysourcesevaluatedduringFY17.
1. Thereisaclearopportunityandneedtoaddressthechallengeofintegratingsoftwareandhardwareforfuturemobilityapplications,particularlymobilityuse-case-drivenR&Dgapsinsoftware-enabled,V2Xsystemsareexpectedtogrowsignificantly.
2. AtaxonomyorroadmapofR&Dgapsinautonomousandconnectedvehiclesisneededtodepicttheattributesandcategoriesoftechnicalgaps.Inthisregard,thereisanopportunitytoleadR&Ddatacollectorstocollect,categorize,andquantifythepriorityorrelevanceofspecifictypesofR&Dgaps–especiallyinthefast-changingandgrowingLiDAR,sensors,
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autonomousandconnectedvehicleresearchspaces.
3. Supportforengagementinhardware-orientedsensorR&D,includingin:a. Substrate-level(e.g.,increasewafersize)R&D;b. Embeddedelectro-opticsdesignandengineering(e.g.,improve
imagingresolution,resilience,accuracy);c. Glassmaterialsdevelopment(e.g.,addressreductionofREs,andadd
improvedopticsandmachiningproperties);andd. Application-specificelectro-opticalengineering(e.g.,learning-by-
doingmanufacturingandelectricalengineeringforhumantransportapplications,intermsofcost,qualityandperformance).
4. TwogapsintheV2XandIntelligenceNetworkingcategoryareconsensus
gapsanddeserveVTO’simmediateattention;a. R&DonLow-CostGeo-Localizationwith2-3cmaccuracy
i. Opportunityfordesignandengineeringoflow-costsensorsandsensorfusionsystemsthatenablerequiredaccuracyforreliableautonomousvehicleapplications.
b. V2XSoftware-HardwareIntegrationi. Opportunityforguidance,independenttest,validationandunderstandingforsoftware-hardwareintegrationforhighlyspecificAVusecases.
5. ThefollowingtopfiveconsensusgapsintheOtherR&DCollaborationgap
categoryarerecommendedforVTO’simmediateattentiona. FundamentalCompetitionatCoreTechnologyLevel(e.g.,Robotics,
MachineVision,AVSensorRequirements)i. Theneedisformoreclarity,commitment,andinvestmentregardingthecorefiguresofmerit,thebaselinecurrentstateandthetargetsforfutureR&D–alltoacceleratepotentialbreakthroughsinthesefields.
b. ScalabilityofAutonomousVehicleEngineering(Hardware,Software,Multi-Context,GlobalImplementations)
i. TheneedistoinvestigateAVengineeringapproachesacrossaspectrumofoperationalcontexts(e.g.,city,urban,people-transport,things-transport,logistics,on-ground,marine,air,etc.)andtoaddresswherescalablesolutionsarebeingexecuted,arefeasible,andtoidentifyandsharebest-practices.
c. SituationalAwareness:DataRepositoryonTechnologyUsed(AVs,SmartCityandSmartGrid)
i. Theneedisforaverifiable,publicallyaccessibledatasourcethatprovidesmultiplestakeholders(e.g.academics,researchers,investors,inventorsandstate-local-federalpartners)clarityaboutwhoisdoingwhatwhere,inordertocatalyzefacts-baseddecision-makinginthisimportantandgrowingfield.
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d. SystematicAssessmentofAVTechnologyGapsi. Theneed,asdiscussedabove,isforasystematic,independentlyderivedviewonthetechnologygapsinautonomousandconnectedvehicleapplications.
ii. Atechnologyroadmapisrecommendedbasedonthehigh-scoreforthisgap.
e. NeedforOpto-ElectronicEngineers(includingforLiDAR,Optics,SensorDevelopmentEngineering)
i. TheprobabilitythatAVtechnologies,likeLiDAR,willbeavailableatsignificantlylowercostsforwidespreadapplicationdependssignificantly(indeed,thisisafirstandforemostdriver–basedonnumeroussources)ontheavailabilityofaskilledworkforcetorampupproduction,reducecostsandmaintainqualityofendproducts.
6. TheavailabilityofskilledAV-domainmanufacturingengineersisviewedasa
keyconstrainttogrowthincapacity(esp.ifthegrowthisrapid)ofautonomousvehicletechnologies(includingLiDAR)intheUS.
DisruptiveTechnologyRecommendationsTheextensivedatacollectionandanalysisinFY17suggeststhattherearedisruptivetechnologyR&DopportunitiestoconsiderinthefieldsofLiDAR-relatedsensors,connectedvehicles,andV2X.ThedataalsosuggeststhattheremaybeuniqueopportunitiesforUS-basedjobcreationbyfocusingonsuchleap-aheadinnovations.“Disruptive”isdefinedastechnologieswhichpresentopportunitiestoaddresssignificantcost-orcostandperformancegapclosingneedsbyoneormoreordersofmagnitude,inwhichcurrenttechnologyistoocostlytoscaletoaddresstheradicalperformanceincreasesandcostreductionsthatareneeded.EachofthefollowingrecommendationsregardingdisruptivetechnologyreflectsSynthesis’independentassessmentofbothprimaryandsecondarysourceresearchcompletedunderthiswork-plan.
7. NewI/Ocontrolarchitecturescanaddressmultipleon-vehicleintelligenceprocessingfunctions,athuman-transportquality,infractionoftimeandfractionofcost.Forexample,fusionofalldomaincontrolunits(DCUs)inonecentralizedvehiclecomputerORnewvalidated,testedarchitecturesthatpermits“fullsense-and-compute”attheedges.NewI/Ocontrolarchitectureswouldpresent:
a. Asolutionthatremovestherequirementtosenddataoffofvehicle,includingformaplocation,navigationorprocessing.
b. Asolutionthatislighter,faster,moresecuredatageneration,collection,miningandprocessingonvehicle.
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c. Asolutionthatcanscaleintermsofinformationprocessingtomatchthe“everycaramap-makerandeverycaramap-user”paradigm.
8. “PowerbyEthernet:”ThiscoverstheneedtowirelesslychargeAVsinmany
contexts,whilemobileorstationary.Analternativetocostlyproduction,install,maintainandpackageofpowerthrough(heavy)motors,batteriesandwiringharnesses.
9. Acceleratedengineeringof“hardcodedhardwareaccelerators.”Thisincludes–forexample–hardcodeddevicesthatcanenablefleet-wideAVsoftwareupgradesandthatenablesuchsoftwareupgradestobe:
a. Fail-safe;b. Valid;andc. Cybersecure
ConclusionThecombinationofautonomoussystemsandInternetofThings(IoT)demandsnew,in-depthunderstandingoffutureengineeringR&Drequirementsateverylevelofthesystemsengineeringprocess.Informationis(becoming)thenewenergy.NewpartnershipsamongOEMs,Tier1-3s,softwaredevelopers,cyber-securityexperts,researchuniversitiesandfederalR&DlabsareneededtocatalyzeR&Dworkinhardware,softwareandsystems-engineeringfields.Suchnewpartnershipsareneededtoguidethesoftware-basedandinterdisciplinaryworkthatneedstobedonetoadvanceautonomousandconnectedvehicles.Synthesishasexploredtheindustrydatainthisreportonafewtopics,andhasidentifiedseveralfast-growingfieldsthatwillframethenatureofthisnewR&Dreality.Inbrief,futureR&Dwillenablesystemsandcomponentsinvehiclestocommunicateandcomputewithnetworksfromthecomponentupthroughthetransportationgrid,throughtoaglobal-levelgrid.ThisiswhyemergingR&Dgapsarenumerous,andnotonlyhardware-focused.Fromahardwareperspective,autonomousandconnectedvehiclescontinuouslyseeksmaller,morefunctional,morepowerdenseandlowercostdesignsofeverycomponentandsensor.Fromaninformationperspective,rigorousprocessesforcollecting,maintainingandanalyzinginformationaboutthe“informationgains”offutureR&Disneeded.FromaUSjobandmanufacturinggrowthperspective,thisreportfindsthatmoreskillinengineering-to-manufacturingcapabilitiesisneeded.Thissimplymeansmorelearning-by-doing.Thefast-emerging,estimatedmulti-trilliondollarmarketforautonomoussystemsisdirectlyconnectedto(asbothadriverandbeneficiaryof)theInternetofThings.2Semiconductormanufacturersplayakeyroleinthisopportunity.2SeveralgoodsummariesoftheInternetofThings,includingprojections,areavailableat:InternetSociety,at:http://www.internetsociety.org/doc/iot-overview,accessed07-11-2016;CISCO,at:http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf,accessed
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Freeman,Mike,“ConnectedCars:TheLongRoadtoAutonomousVehicles,SanDiegoTribune,03-31-17,http://www.sandiegouniontribune.com/business/technology/sd-fi-connected-automobiles-20170330-story.html,accessed06/17.Fujitsu,"SmartAutomobilesandNext-GenerationTransportationSystems",06-15-15,http://journal.jp.fujitsu.com/en/2015/06/15/02/,accessed03/14/17.Gartner,John,“RoadTeststoProvideCriticalVehicleCommunicationsData”,NavigantResearch,02-02-17,https://www.navigantresearch.com/blog/road-tests-to-provide-critical-vehicle-communications-data,accessed06/17.Gershgorn,Dave,"AfterTryingtoBuildSelf-drivingTractorsforMorethan20Years,JohnDeerehasLearnedaHardTruthaboutAutonomy",08-02-17,Quartz,https://qz.com/1042343/after-trying-to-build-self-driving-tractors-for-more-than-20-years-john-deere-has-learned-a-hard-truth-about-autonomy/,accessed08-03-17.Giampapa,JosephAndrewet.al,“AccessibleTransportationTechnologiesResearchInitiative(ATTRI):StateofthePracticeScan”,April,2017,http://ri.cmu.edu/wp-content/uploads/2017/04/1_ATTRI_SOP_2017-04.pdf,accessed07-18-17.Gillespie,Mark,"OhioAutoTestingFacilitytoGet$45MtoGrow",AssociatedPress,01-27-17,http://www.mariettatimes.com/news/2017/01/ohio-auto-testing-facility-to-get-45m-to-grow/,accessed05-22-17.GovTech,"SanJose,Calif.,ReleasesAutonomousVehicleRFI",http://www.govtech.com/San-Jose-Calif-Releases-Autonomous-Vehicle-RFI.html,accessed07-17-17.GreenCarCongress,“AutonomousElectricVehicleMakerNAVYAPicksSaline,MIforFirstUSPlant",07-18-17,http://www.greencarcongress.com/2017/07/20170718-navya.html,accessed07-18-17.GreenCarCongress,"BaiduandNVIDIATeamuponCloud-to-carPlatformforSelf-drivingCars;HDMaps,Level3Control,AutomatedParking",09-01-16,http://www.greencarcongress.com/2016/09/20160901-baidu.html,accessed09-06-16.GreenCarCongress,"FordTakesStakeinCivilMaps;3DMappingTechnologiesforFullyAutonomousVehicles;AIandVoxelHashing”,07-16-16,http://www.greencarcongress.com/2016/07/20160715-civilmaps.html,accessed07-18-16.
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GreenCarCongress,"GoogleMapsAcquiresMappingAnalyticsStartupUrbanEngines;ImprovedUrbanMobility",09-18-16,http://www.greencarcongress.com/2016/09/20160918-urban.html,accessed09-19-16.GreenCarCongress,"NVIDIADivesintoParkerMobileProcessorforNextGenerationofAutonomousVehicles",08-23-16,http://www.greencarcongress.com/2016/08/20160823-parker.html,accessed08-23-16.GreenCarCongress,"NXP,CohdaWirelessRoadLINKAchievesTopPerformanceinAudiFieldTrialforIntelligent,SecurelyConnectedVehicles",01-07-15,http://www.greencarcongress.com/2015/01/20150107-roadlinkl.html,accessed05-17-17.GreenCarCongress,"QualcommandLearCorporationSignCommercialWirelessElectricVehicleChargingLicenseAgreement",07-27-16,http://www.greencarcongress.com/2016/07/20160727-halo.html,accessed07-28-16.GreenCarCongress,"QualcommandTomTomPartneronCrowdsourcingHigh-definitionMappingDataforAutonomousDriving",GreenCarCongress,02-27-17,http://www.greencarcongress.com/2017/02/20170227-qualcomm.html,accessed05-18-17.GreenCarCongress,"QuanergyAcquiresOtusPeopleTrackerSoftwarefromRaytheonBBNforAdvancedAutonomousDrivingandSecurityLiDARApplications",08-29-16,http://www.greencarcongress.com/2016/08/20160829-otus.html,accessed08-31-16.GreenCarCongress,"VolvoCarsandUberJoinForcestoDevelopNext-GenAutonomousDrivingCars;$300MJointProject",08-18-16,http://www.greencarcongress.com/2016/08/20160818-volvouber.html,accessed08-23-16.Greenstein,Bret,etal.,"UnderstandingOlli:ALeapAheadforSelf-DrivingVehicles",Webinar,08-10-16,11a.m.EDT,https://vts.inxpo.com/scripts/Server.nxp?LASCmd=AI:4;F:QS!10100&ShowKey=33365&AffiliateData=[site]&Referrer=http%3A%2F%2Fwww.autonews.com%2Fsection%2Foem01%26profilename%3Dmanufacturing,accessed07-20-16.Groothuis,Steve,"AutonomousVehicles:SuccessfullyIntegratingNewChips,Packages&Modules",AutonomousVehicleSiliconValleyWorkshop,02-28-17,https://drive.google.com/file/d/0B0A3-bBPmuU1QjZrTGlseDdRNVU/view,accessed04-25-17.
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November2017 SynthesisPartners,LLC© 34
Groothuis,Steve,“AutonomousVehicles:SuccessfullyIntegratingNewChips,Packages&Modules,”Samtec,08-22-16,http://suddendocs.samtec.com/notesandwhitepapers/autonomous_vehicle_groothuis_samtec_r4_aug_22_2016.pdf,accessed02-27-17.Holland&KnightLLP,"FederalFundingOpportunitiesforConnectedandAutonomousVehicleTechnologies",05/15/17,http://www.lexology.com/library/detail.aspx?g=a81fd3d6-00fb-43bb-bac2-18b33cbb7959,accessed06-27-17.IBMCenterforAppliedInsights,"DigitalDisruptionandtheFutureoftheAutomotiveIndustry",November2015,http://www.slideshare.net/ibm/digital-disruption-and-the-future-of-the-automotive-industry-54826461,accessed06-28-16.IBMGlobalBusinessServices,"CloudforAutomotive",https://www-935.ibm.com/services/multimedia/Cloud_for_Automotive_Exec_Summary.pdf,accessed03-03-17.Innoviz,"InnovizTechnologiesSelectedfromAmong300CompaniestoParticipateinStartupAutobahn2017",PressRelease,02-15-17,https://www.innoviz.tech/single-post/2017/02/15/Innoviz-Technologies-Selected-from-Among-300-Companies-to-Participate-in-Startup-Autobahn-2017,accessed05-22-17.Intel,"IntelCapitalAnnounces$38MofNewInvestmentsin12TechnologyStartupstoKickOffGlobalSummit",PressRelease,10-24-16,https://newsroom.intel.com/news-releases/intel-capital-announces-38m-new-investments/,accessed03-02-17.InternationalTransportForum,"SharkTank-ThinkingAboutSettingUpaPilotProgram?WhatYouNeedtoSucceed-LessonsLearntfromEC-fundedProjects–andBeyond",https://higherlogicdownload.s3.amazonaws.com/AUVSI/14c12c18-fde1-4c1d-8548-035ad166c766/UploadedImages/documents/Breakouts/01-1%20Public%20Transport%20and%20Shared%20Mobility.pdf,accessed06/17.Irwin,John,"Delphi,MobileyetoPartneronAutonomousDrivingSystem",08-23-16,AutomotiveNews,http://www.autonews.com/article/20160823/OEM06/160829963/delphi-mobileye-to-partner-on-autonomous-driving-system,accessed08-24-16.Irwin,John,"ToyotaExecutiveMapsouttheFutureof'ComputersonWheels'",AutomotiveNews,08-03-16,http://www.autonews.com/article/20160803/OEM06/160809910/1128,accessed08-04-16.
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Appendix:ListOfR&DGapsIdentifiedInFY17,InAlphabeticalOrder
1. 3rdPartyDevelopment,TestingandValidationofAVEdgeScenarios
2. Accessto/TransparencyofDatafromAVFleetManagers
3. AVMobilityCommandCenters(e.g.,ATCCenters)
4. BalancingVehicleDesignTimewithSoftwareUpgradeTime-Cycles
5. CatalyzeOEMtoBeSpectrumProviders
6. CollaborationonLessonsLearnedfromReal-WorldAVImplementation
7. CollaborativeConnectedVehicleTest-Beds
8. CyberSecurity:SecureandReliableV2XConnectivity
9. DrivingTaskModelDefinition:ForHuman-to-MachineTransfer
10. FundamentalCompetitionatCoreTechnologyLevel(e.g.,Robotics,MachineVision,AVSensorRequirements)
11. HDMaps:Development,ValidationandStandardization
12. IndiumPhosphide6"SubstrateDevelopment
13. LowCostV2XConnectivity
14. MemorySupplyChainBottleneck
15. Memory-ComputeTechnology
16. MiniaturizedProcessors
17. ModeTransfer(HumantoMachineControl)
18. ModelingandSimulationofAVCoding/SoftwareArchitectures:Guidelines,TestingandValidation
19. NeedforOpto-ElectronicEngineers(Incl.forLiDAR,Optics,SensorDevEngineering)
20. OnboardScalableLow-CostPowerandProcessing(toDriveUpdating,MachineLearningOn-Vehicle)
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21. OpenSourceCodeAVArchitectures
22. OpticalMaterialsDevelopment(incl.accesstoREs)
23. Over-the-airAVSoftwareUpgrades:Testing&Validation
24. PhotonicsforMemory,ComputeandDataTransfer
25. R&DonLow-CostGeo-LocalizationWith2-3cmAccuracy
26. R&DonPedestrianAVandVRU,PedestrianInteractions
27. ScalabilityofAVEngineering(Hardware,Software,Multi-Context,GlobalImplementations)
28. Scalable,Low-CostAVBlackBoxes
29. SensorCostReduction
30. SensorFusion
31. SensorsforComplexDrivingEnvironments
32. SituationalAwareness:DataRepositoryonTechforAVs,SmartCity,SmartGrid
33. StrategicPlanning:SmartCars,SmartCities,SmartGrid
34. SystematicAssessmentofAVTechnologyGaps
35. TechnologyTutorials(Deepdives,withAVrequirementsdefined)
36. V2XInteroperability&Standards
37. V2XSoftware-HardwareIntegration