高盛-新的AI技术能否加速自动驾驶落地-2024-08-自动驾驶

10July2024|5:03PMEDT

Someinvestorsandmarketobserversarguethattheindustryisonthecuspofa

MarkDelaney,CFA

+1(212)357-0535|

breakthroughinautonomousvehicle(AV)scalingdrivenbynewAIandGPU

mark.delaney@GoldmanSachs&Co.LLC

technology,whileothersarguethatbroad-basedAVdeploymentsmayneveroccur.

KotaYuzawa

+81(3)4587-9863|kota.yuzawa@

Thebottom-lineisthatwebelieveimprovedAItechnologywillhelpthe

GoldmanSachsJapanCo.,Ltd.

industryreachhigherlevelsofperformance,althoughwealsobelievethat

AllenChang

+852-2978-2930|allen.k.chang@

widescaleAVadoptionisstillatleastafewyearsawayasabasecase.We

GoldmanSachs(Asia)L.L.C.

believethatgloballevel3(L3)ADASpenetration(e.g.vehiclesthatcan

GeorgeGalliers

+44(20)7552-5784|

situationallyhaveeyes-offdriving,suchasonahighway)willreach10%ofthe

george.galliers@

GoldmanSachsInternational

marketfornewvehiclesalesin2030,withlevel4orAVs(e.g.eyes-offina

TinaHou

givenarea,suchasarobotaxiinacity)at2.5%in2030.Thisimpliesthatlevel4

+86(21)2401-8694|

tina.hou@

(L4)volumeswillcontinuegrowing,albeitgenerallyforcommercialusecases

GoldmanSachs(China)SecuritiesCompanyLimited

likerobotaxisintheneartointermediateterm.

EricSheridan

+1(917)343-8683|eric.sheridan@

WeassumemostoftheADAS(advanceddriverassistancesystems)andAVindustryvolumegrowthinthenextfewyearswillcomefrompartially

GoldmanSachs&Co.LLC

ToshiyaHari

+1(646)446-1759|toshiya.hari@

autonomousL2/L2+vehiclesthatrequiredriversupervision.Weassume

GoldmanSachs&Co.LLC

DaikiTakayama

L2/L2+mixwillrisefromabout20%ofsalesthisyeartoabout30%in2027.

+81(3)4587-9870|

daiki.takayama@

GoldmanSachsJapanCo.,Ltd.

OurAVforecastimpliesthataglobalfleetofafewmillioncommercialAVs

BenMiller

usedforridesharecouldbeontheroadin2030.Althoughthiswouldcomprise

+1(917)343-8674|

ler@

lessthan1%oftheglobalcarparcofover1bnvehicles,itcouldresultina

GoldmanSachs&Co.LLC

>$25bnmarketforpersonalmobilityfromrobotaxis.

LincolnKong,CFA

+852-2978-6603|lincoln.kong@GoldmanSachs(Asia)L.L.C.

WebelievethatstocksinvestorsshouldownonthisthemeincludeNvidia,Uber,

JerryRevich,CFA

Mobileye,Renesas,Baidu,DesaySVandQuanta.

+1(212)902-4116|jerry.revich@GoldmanSachs&Co.LLC

GivenadvancesinAItechnology,includingthelatestNvidiaprocessorsaswellas

DanielaCosta

+44(20)7774-8354|

differenttrainingapproaches(e.g.afully“end-to-end”approachofcamera/sensor

daniela.costa@

GoldmanSachsInternational

inputsinanddrivingpolicyoutthatcanpotentiallyhelpsolvedifficultedgecases

VerenaJeng

especiallyifthereisenoughdata,ora“compoundapproach”thatutilizesmachine

+852-2978-1681|verena.jeng@GoldmanSachs(Asia)L.L.C.

learning/AIbutwithsubsystemsthatcanallowforefficiencyandverificationofthe

RyoHarada

solution),weattempttobetterunderstandiftherateofprogresstoward

+81(3)4587-9865|ryo.harada@GoldmanSachsJapanCo.,Ltd.

wider-scaleadoptionofautonomywillaccelerate,includingL3andL4.Seeour

AlexanderDuval

+44(20)7552-2995|

GoldmanSachsdoesandseekstodobusinesswithcompaniescoveredinitsresearchreports.Asaresult,

investorsshouldbeawarethatthefirmmayhaveaconflictofinterestthatcouldaffecttheobjectivityofthis

report.Investorsshouldconsiderthisreportasonlyasinglefactorinmakingtheirinvestmentdecision.ForRegACcertificationandotherimportantdisclosures,seetheDisclosureAppendix,orgoto

..i//eS.html.Analystsemployedbynon-USaffiliatesarenotregistered/qualifiedasresearch

GlobalAutos&IndustrialTech

10July20242

“AI101”sectionofthisreportformoredetailsonAIapproacheslikeend-to-end.

WiththisreportweupdateourglobalADASandAVforecastsbasedoninputsfromourglobalauto,industrial,andTMTteammembers.

Keyworkinthisreport-

1)ExaminationofTesla’stechnicalprogress,andprogressofvariousAVcompanyefforts

2)IllustrativecostpermileforanAVrobotaxibusinesswithaverticallyintegratedmodel,andadiscussiononrideshareandAVbusinessmodels

3)UpdatedglobalADAS/AVforecast

4)Discussionofnewelectronicarchitecturesandimplicationsforsemis

5)2030EPSscenarioanalysisforTesla

PMSummary

Wethinkitisnotablethattherearenowautonomousvehiclesontheroad.ThereareasmallnumberofAVsoperatinginpartsofmajorcitiessuchasSanFrancisco,

Phoenix,BeijingandWuhan.

However,thetechnologyhasyettobebroadlydeployed.Keyissuesgatinggrowtharerelatedtounderstandingcomplextraffic/drivingscenarios(oredgecases),along

withbusinessandregulatoryfactors.WhileAVsfromcompaniessuchasBaidu(ApolloGo),WaymoandPony.aimayalreadybesaferintermsofaccidentspermilethanhumandriverswithingeofencedareas(e.g.perdatafromleadingcompaniessuchasWaymo,albeitwithAVsontheroadtodaytypicallyabletoqueryahumaninaremotelocationforassistanceifneeded),therecontinuetobecaseswhereAVsgetconfusedorstuckinscenariosahumancouldlikelynavigate.

Weseektobetterunderstandwherethetechnologyandindustrycurrentlystands,andifnewAItechnologycanhelptoaccelerateprogresstowardwider-scaleadoptionofL3(e.g.situationallyeyes-offandhands-offdriving,suchasonahighway)orL4(e.g.

eyes-offinagivenarea,suchasarobotaxiinacity)autonomy.

ResearchonAIscalingdoessuggestthataddedcompute,largertrainingdatasets,

andimprovedmodelarchitecturesshouldcontributetobetterAImodel

performance.MicrosoftCEOSatyaNadelladuringakeynoteatMicrosoftBuild2024notedthatsimilartohowMoore’sLawdrovetechnologyinthepast,AItechnologynowallowscomputeperformancefortrainingdeepneuralnetworkstoroughlydoubleevery6months(referencingresearchfromEpoch).

WeconsiderTeslatobeoneoftheleadersinautonomoustechnology.Tesla’ssupervisedfullselfdriving(FSD)technology,whichisanL2/L2+systemasit

requiresthedrivertopayattentionandbepreparedtotakeoveratalltimes,isalreadysaferthanvehiclesdrivenmanuallyintermsofaccidentspermiledatafromTesla(Exhibit1).Tesla’sdatashowsthatin2023accidentswithsupervisedFSD

GlobalAutos&IndustrialTech

10July20243

(whichisTesla’ssolutionthatwillallowthecartodopointtopointnavigation,andworksonhighwaysandcitystreets)occurredevery4to5millionmiles,andaccidentson

Autopilot(whichincludesmorebasicfeaturesliketrafficawarecruisecontrolandlanekeeping)occurredevery5to6mnmiles(althoughthesearegenerallyhighwaymiles),comparedtoonceevery600-700KmilesfortheUSonaverage.In1Q24,accidentswithAutopilothappenedonceevery7-8millionmiles.

Exhibit1:TeslaADASsystemmilesdrivenbeforeanaccident

Milesdrivenbeforeaccident(mns)

6

5

4

3

2

1

0

201820192020202120222023

TeslaFSDBeta/SupervisedTeslaAutopilotTeslanon-AutopilotUSaverage

Source:Companydata,FHWA,NHTSA,GoldmanSachsGlobalInvestmentResearch

Whileweconsiderthesesafetystatisticstobeimportant,measuringifTesla

driversgetinfeweraccidentswhileusingthetechnologywithactivedriver

supervision(andtakingoverifthereisanissue)isnotthesameaswhetherthevehicleswouldbebetterand/orsaferwhenunsupervised(e.g.L3/L4capability).

Wethereforebelieveitisalsohelpfultogaugethepercentageofdriveswithno

interventionsandinterventionspermiletoassesswhenTeslamayreachunsupervisedcapability(e.g.L3orL4).Thereareotherperformancemetricsbeyondjustaccidentstoconsider(e.g.properlaneselection,smoothdrivingbehavior,andrespondingto

emergencyvehicles).

WhileTesladoesn’tdiscloseinterventiondataonFSD(andinterventionsareatthe

discretionofthedriverwhichcancomplicateananalysisofthissort),somedrivers

submitinterventiondatatoTeslaFSDtrackerandthisshowsthatthepercentageof

driveswithoutaninterventionhasgoneup,andabout70%ofdriveswiththelatestversionofFSDhavebeenno-interventionperthiscrowd-sourceddata(Exhibit2).Tesla’slatestFSDversion(V12)wasdevelopedwithmorerelianceonAI,oran

“end-to-end”approachofvision/camerainputsinanddrivingpolicyoutthatcanpotentiallyhelpsolvedifficultedgecasesespeciallyifthereisenoughdata.

Moreover,thissamedatasuggestscriticalinterventionsoccurevery~300miles

withFSD.Whilewethinkitwouldbewrongtoassumethateverydisengagementwouldhaveresultedinacrash,italsosuggeststhetechnologycouldbesome

timeofffrombeingL3orL4giventhe600-700KmilestraveledbetweenaccidentsforthetypicalvehicleintheUS.OurownrecentridesinFSDenabledvehiclesalso

suggestthatFSDisimpressivebutnotyetreadytobeL3orL4,inouropinion.

10July20244

Exhibit2:PercentofTesladriveswithoutaninterventionpercrowdsourceddata

100%90%80%70%60%50%40%30%20%10%

0%

Feb-22

May-22

Aug-22

Nov-22

Feb-23

May-23

Aug-23

Nov-23

Feb-24

May-24

Aug-24

Nov-24

Feb-25

May-25

Aug-25

Nov-25

Feb-26

May-26

Aug-26

Nov-26

Feb-27

May-27

Aug27

%ofdriveswithnodisengagements--------SteadyFSDscalingRapidFSDscaling

----FSDhitsceiling

Source:TeslaFSDTracker,GoldmanSachsGlobalInvestmentResearch

ExtrapolatingtherateofnointerventiondrivesimpliesinourviewthatTeslacouldreachL3onhighwaysatfullspeeds,atleastinclearweather,inthenexttwotothreeyears,especiallyifTesla’ssignificantinvestmentsintechnology(Teslaisspending$3-$4bnonNvidiacomputethisyear)anduseofanend-to-endAIapproachhelp.Giventheaddedcomplexitiesofoperatingindenseurbanenvironmentsandhighbarforsafetyin

unsuperviseddriving,weassumegeneralizedL4wouldtakelongertoreach.Webelievethatutilizinghumanassistanceinaremotelocation(similartocurrentrobotaxieffortslikeWaymoandBaidu)couldallowTeslatoreachL4functionalitysoonerthanitis

otherwisetrackingtoalbeitwithaddedcostandscalingchallenges.

LookingatAVeffortsbesidesTesla,whileaccidentdatahasbeenpromising(e.g.

Waymo’sanalysisshowsatleast57%fewervehiclecrashespermilethanahuman

driverwithitsL4robotaxis),thecurrentdeployments(e.g.fromWaymo,Pony.aiandBaiduApolloGo)havethusfarbeentargetedintermsofdeployments,andlimitedtocertainsectionsofselectcities(wehavemoredetailsinthe“CompanyADAS

andAVefforts”sectionofthisreport).Whilethismaybedueinparttotechnologyscalabilityissues(e.g.relyingondetailed3-Dmaps,needinghumanremoteassistanceattimes,anddifficultyinhightraffic/complexscenarioslikeconstruction),wealso

believeeconomicconsiderationsareafactor.AsweshowinExhibit3,thecostsper

milearelikelyveryhighatlowvolumesdueinparttothecostofthehardware/computeandhumanremoteassistance(notethatTeslacouldhaveacostadvantagewithits

AV/ADAStechnologygivenitsverticalintegrationforinferencechips,scale,anditslimitedsensorsuite).

Assumingthatdepreciationandinsurancecostsnormalizetolevelsonparwithhumandrivencommercialrideshareentities,weestimatethatvehicledrivingcostspermileforanAVat50-75kmilesdrivenperyearpervehicleandroughly10carsperremote

operatorcouldreach~$1.00permile(weshowthisforillustrativepurposesoccurringinthe2030timeframeinExhibit3).Costswithcorporateoverhead/R&Dwouldbehigher.Longer-termthesecostscoulddecline.

GlobalAutos&IndustrialTech

10July20245

Exhibit3:IllustrativecostmodelforaverticallyintegratedAVridesharecompany

2023

2024E

2025E

2030E

2035E

2040E

AVcostpervehicle($US)

125,000

100,000

85,000

50,000

50,000

50,000

Milesdrivenpercar

22,500

25,000

27,500

75,000

100,000

125,000

Vehiclesinserviceyearend

177

259

478

2,570

18,597

72,967

Wagesperremoteoperator

76,875

78,797

80,373

87,870

94,661

99,982

Carsperoperator

3

3

3

10

30

35

Vehicledrivingcosttpermile

$3.35

$3.13

$2.94

$0.98

$0.70

$0.58

R&D($USmn)

825

908

998

1,521

1,960

2,502

SG&A($USmn)

230

265

304

612

1,127

1,479

Tottalcosttpermile

$268.71

$184.11

$102.03

$12.04

$2.36

$1.02

Source:GoldmanSachsGlobalInvestmentResearch

Forcontext,Lyftcommittedtopayingdriversatleast70%ofriderpaymentsperweekafterexternalfeeslikecommercialinsurance(weestimate~$0.30permile)are

subtracted,andLyftestimatesthatthereare~$0.31/mileofexpensesassociatedwithoperatingthecarforthedriver(i.e.fuelcosts,maintenance,cleaning,anddepreciation).WenotethattheaveragecosttoownapersonalcarintheUSis~$0.80permileperAAA,assuming15Kmilesdrivenperyear.

Inaddition,ourrecentdiscussionswithAVoperatorsinChinasuggestthatAV

companiessofarneedtochargeadiscounttoconventionalrideshareplatformssuchasDiDiofatleast30%inordertoattractuserstothenetworkandtocompensatefor

factorslikelimitstothelocationsthatAVscanpresentlyreachduetogeofencingrestrictions.

Finally,regulatory/liabilityandsocietalconcernsmaygatetherateofgrowth.For

example,Cruisepausedcommercialoperationsafterasevereaccidentinvolvingoneofitsrobotaxis,andcommunitiesmayhaveamuchhighersafetybarforAVsthancurrenthumanperformance.

AsabasecasewenowassumethatgloballyL3enabledvehiclescouldreach10%ofindustryunitsalesvolumesin2030,andthatL4willbe2.5%in2030drivenbycontinuedtechnologicaladvancement(includingfromAI)andlowercostsofrelevanthardware(e.g.lidarandtheintroductionofpurposebuiltAVplatforms).However,ifthelatestAItechnologyhelpstheindustrytoaccelerateautonomousvehicledevelopmentfasterthanweexpect,thenwebelievethiscouldoccurafewyearsearlier.

GlobalAutos&IndustrialTech

10July20246

Exhibit4:GlobalADASandAVL3-5penetrationrateasapercentofnewlightvehiclesales

Globallevel3/4/5penetrationrate

70%

60%

50%

40%

30%

20%

10%

0%

BullBase

Source:Companydata,GoldmanSachsGlobalInvestmentResearch

OurAVforecastimpliesthataglobalfleetofafewmillioncommercialAVsused

forridesharecouldbeontheroadin2030.Althoughthiswouldcompriselessthan1%oftheglobalcarparcofover1bnvehicles,itcouldresultina>$25bnmarketforpersonalmobilityfromrobotaxis(dependingonfactorssuchasASPs,tripsper

day,andaveragemilestraveledpertrip).

Exhibit5:Weestimatethemarketin2030forrobotaxiscouldbe>$25bn

Revenuepertrip

Tripsper

robotaxiperday

150

GlobalAVsinoperation(000s)

1,3502,0002,650

2030marketscenariosforrobotaxis($mn)

750

3,300

4,000

$5

2

$548

$2,738

$4,928

$7,300

$9,673

$12,045

$14,600

4

$1,095

$5,475

$9,855

$14,600

$19,345

$24,090

$29,200

6

$1,643

$8,213

$14,783

$21,900

$29,018

$36,135

$43,800

8

$2,190

$10,950

$19,710

$29,200

$38,690

$48,180

$58,400

10

$2,738

$13,688

$24,638

$36,500

$48,363

$60,225

$73,000

12

$3,285

$16,425

$29,565

$43,800

$58,035

$72,270

$87,600

14

$3,833

$19,163

$34,493

$51,100

$67,708

$84,315

$102,200

$7

2

$767

$3,833

$6,899

$10,220

$13,542

$16,863

$20,440

4

$1,533

$7,665

$13,797

$20,440

$27,083

$33,726

$40,880

6

$2,300

$11,498

$20,696

$30,660

$40,625

$50,589

$61,320

8

$3,066

$15,330

$27,594

$40,880

$54,166

$67,452

$81,760

10

$3,833

$19,163

$34,493

$51,100

$67,708

$84,315

$102,200

12

$4,599

$22,995

$41,391

$61,320

$81,249

$101,178

$122,640

14

$5,366

$26,828

$48,290

$71,540

$94,791

$118,041

$143,080

$9

2

$986

$4,928

$8,870

$13,140

$17,411

$21,681

$26,280

4

$1,971

$9,855

$17,739

$26,280

$34,821

$43,362

$52,560

6

$2,957

$14,783

$26,609

$39,420

$52,232

$65,043

$78,840

8

$3,942

$19,710

$35,478

$52,560

$69,642

$86,724

$105,120

10

$4,928

$24,638

$44,348

$65,700

$87,053

$108,405

$131,400

12

$5,913

$29,565

$53,217

$78,840

$104,463

$130,086

$157,680

14

$6,899

$34,493

$62,087

$91,980

$121,874

$151,767

$183,960

Source:Companydata,GoldmanSachsGlobalInvestmentResearch

TherearealsosignificantsocietalbenefitsfromADASandAVtechnology,asabout40kpeopledieintrafficfatalitiesintheUSannuallyperNHTSA,andover1millionpeopledieeachyeargloballyinaccidentspertheWorldHealthOrganization.

GlobalAutos&IndustrialTech

10July20247

AI101-WhatisAI,andwhat’sthedifferencebetween“end-to-end”anda“compoundapproach”?

Inthissectionofthereport,weexplainkeyconceptsforartificialintelligence,asdetailedinapriorreportledbyToshiyaHari.

ArtificialIntelligence:Artificialintelligencedescribesascienceofsimulatingintelligentbehaviorincomputers.Itentailsenablingcomputerstoexhibithuman-likebehavioraltraitsincludingknowledge,reasoning,commonsense,learning,anddecisionmaking.

Machinelearning:Machinelearningisabranchofartificialintelligenceandentails

enablingcomputerstolearnfromdatawithoutbeingexplicitlyprogrammed.For

example,thecomputerlearnshowtoidentifyanobjectsuchasadogoracatwithdata.

Neuralnetworks:AneuralnetworkinthecontextofAI/machinelearningdescribesatypeofcomputerarchitecturethatsimulatesthestructureofahumanbrainontowhichAI/machinelearningprogramscanbebuilt.Itconsistsofconnectednodesinaggregatethatcansolvemorecomplexproblemsandlearn,liketheneuronsinahumanbrain,suchasinExhibit6.Theprocessofbackpropagationisusedinmachinelearningto

adjusttheweightoftheneuronsintheneuralnetandstrengthenthepathstoproduceacorrectanswer(e.g.toidentifyanobject).

Exhibit6:Illustrativeneuralnetframework

Source:GoldmanSachsGlobalInvestmentResearch

Deeplearningisasubsetofmachinelearningwithahierarchyoflayersinaneuralnet,withdeeplearninghavingmorelayers.Forexampleinthedogorcatexample,differentlayerscouldcorrespondtothekeydefiningfeaturesofagivenanimal.

CompoundAIsystemvs.end-to-endapproach:TherearedifferentapproachestoAIdevelopmentinthecontextofautonomousdriving,withonebeinganend-to-enddesign(asdescribedmorefullyinthisarticle),whichistheapproachTeslahasmovedtoandisasinglemodelthattakesthevisioninputsinanddirectlyoutputsthedrivepolicylike

steeringandbraking.Itreducestheneedforhumancoding,andmayhelpsolvethe

GlobalAutos&IndustrialTech

10July20248

kindofedgecasesthathavelimitedAVprogressthusfar.AnalternativeapproachisacompoundsolutionthatcanutilizeAIfordifferentsubsystemsorinputs.Thesolutioncanthenusethistogetherwithgluecodeand/oroverlayotherrules/factors(e.g.

ensuringcertaintrafficlawsareobeyed).WhilethereisadebateifthiswilllimitwhatAIcanachieveintermsofhardtounderstandedgecasescenarios,itcanhaveefficiencies(e.g.ChatGPTdoesn’tneedtouseAItrainingtosolvebasicmath,itcanjustquerya

calculatormodule,asdescribedinthisblogontheprosandconsofanend-to-endvs.acompoundapproach)andacompoundsystemcanbeeasiertounderstand/verify(whichisespeciallyimportantindrivingasamistakefromAIcouldbefatal).CompoundAI

approachesstillmakeuseofadvancedAItechniquesliketransformers.Wealsonote

thatablendofapproachescanbeused,suchasanend-to-endmodelasaconsiderationforthedrivingdecisionsbutwithcertainpolicyrulesthatareprogrammed.

Willmorecomputeandanend-to-endapproachhelpTesla’sFSDprogress?

Teslahadexpectedtohavefullyautonomousvehiclesreadyin2020per

commentsatits2019AutonomyInvestorDay,andtheindustrymorebroadlyhasstruggledtomeetitsAVtargets.InthissectionwediscussifnewerAItechnologycanhelpTeslatomeetthisobjective.

Specifically,inMay2023,TeslaannouncedonXthatitwaswouldadoptanend-to-endAIapproachwithVersion12ofitssupervisedFullSelfDriving(orFSD)product.Recallthatanend-to-endapproachisvisionin(fromthecameras)anddrivingpolicy(suchassteeringandbraking)out.Moreover,onits1Q24earningscall,Teslacommentedthatitwasnolongercomputeconstrained.

ResearchonAIscalingdoessuggestthataddedcompute,largertrainingdatasets,andimprovedmodelarchitecturesshouldcontributetobetterAImodelperformance.

MicrosoftCEOSatyaNadelladuringakeynoteatMicrosoftBuild2024notedthatsimilartohowMoore’sLawdrovetechnologyinthepast,AItechnologynowallowscomputeperformancefortrainingdeepneuralnetworkstoroughlydoubleevery6months

(referencingresearchfromEpoch).

Oncompute,Teslaisexpectingtohavecommissionedandinstalledaround85KH100GPUsfromNvidiabytheendof2024,upfrom35Kasofits1Q24earningscall.Teslaplanstoinvestabout$3-$4bninNvidiahardwarein2024.TheH100improves

performancebyupto9XforAItrainingandupto30XforAIinferenceovertheprior

A100GPUforlargelanguagemodel(LLM)transformerdevelopmentperNvidia.

Additionally,NvidianotedthattheupcomingGB200withBlackwellcouldofferupto30XbetterperformancecomparedtothesamenumberofNvidiaH100sforLLMinferenceworkloadswithupto25Xlowercostandenergyconsumption.

WeconsiderTeslatobeoneoftheleadersinautonomoustechnology.Tesla’s

supervisedFSDtechnology,whichisanL2/L2+systemasitrequiresthedrivertopayattentionandbepreparedtotakeoveratalltimes,isalreadysaferthana

10July20249

humandriverintermsofaccidentspermileperdatafromTesla(Exhibit7).Tesla’sdatashowsthatin2023accidentswithFSD(whichisTesla’ssolutionthatallowsthecartodopointtopointnavigation,andworksonhighwaysandcitystreets)occurredevery4to5millionmiles,andaccidentsonAutopilot(whichincludesmorebasicfeaturesliketrafficawarecruisecontrolandlanekeeping)occurredevery5to6mnmiles(althoughthesearegenerallyhighwaymiles),comparedtoonceevery600-700KmilesfortheUSonaverage.In1Q24,accidentswithAutopilothappenedonceevery7-8millionmiles.

Exhibit7:TeslaADASsystemmilesdrivenbeforeanaccident

Milesdrivenbeforeaccident(mns)

6

5

4

3

2

1

0

201820192020202120222023

TeslaFSDBeta/SupervisedTeslaAutopilotTeslanon-AutopilotUSaverage

Source:Companydata,FHWA,NHTSA,GoldmanSachsGlobalInvestmentResearch

Whileweconsiderthisanimportantmetric(withrealworldimplications),

measuringifTesladriversgetinfeweraccidentswhileusingthetechnologywithactivedriversupervision(andtakingoverifthereisanissue)isnotthesameaswhetherthevehicleswouldbebetterand/orsaferwhenunsupervised(e.g.L3/L4capability).Wethereforebelieveitisalsohelpfultogaugethepercentageofdrives

withnointerventions,andinterventionspermile,toassesswhenTeslamayreachunsupervisedcapability(e.g.L3orL4).

WhileTesladoesn’tdiscloseinterventiondataonFSD(andinterventionsareatthe

discretionofthedriverwhichcancomplicateananalysisofthissort),somedrivers

submitinterventiondatatoTeslaFSDtrackerwhichshowsthatthepercentageofdriveswithoutaninterventionhastrendedhigherwithFSDV12(theversionwhenTeslamovedtoanend-to-endAIapproach).About70%ofdrivesonV12havebeen

no-interventionperthiscrowd-sourceddata,whichisanimprovementfromV10andV11.

GlobalAutos&IndustrialTech

10July202410

Exhibit8:TeslaFSDperformancebyversionpercrowd-sourceddata

80%

%ofdriveswithnodisengagements

70%

60%

50%

40%

30%

20%

10%

0%

12

.11.3

10.6911.4

10.11

o10.12

2/1/2022

3/1/2022

4/1/2022

5/1/2022

6/1/2022

7/1/2022

8/1/2022

9/1/2022

10/1/2022

11/1/2022

12/1/2022

1/1/2023

2/1/2023

3/1/2023

4/1/2023

5/1/2023

6/1/2023

7/1/2023

8/1/2023

9/1/2023

10/1/2023

11/1/2023

12/1/2023

1/1/2024

2/1/2024

10.10

Source:TeslaFSDTracker

Moreimportantly,whatdoesthisdatasuggestforwhenTeslacouldreachL3or

eyes-offcapability?Therearedifferentpotentialextrapolationsofthedata,includingrapidscaling,slowerbutsteadyprogress,oraneventualceilingonimprovement.Whilewerecognizethatthisdataisimperfect,itwouldimplyinouropinionthatTeslaistracking