2026GTC-保时捷-2026-03-自动驾驶（英）

Customer-

CentricInnovationin

Automotive12Agenda>

Porsche

Engineering

Company

Overview

>

Engineering

Ecosystem

&

Development

Loop>Use

Cases•SignalFoundationModel•AI-CorrectedRace

GPS•Decoding

theFleet(AgenticDiagnosis)>Summary•

Function

andsoftware

development•

Software

integration

and

software

quality•

BigData

and

Artificial

Intelligence•Testing

and

simulation•System

development

and

integration•Testing

and

simulation•

Function

and

software

development•

Big

Dataand

Artificial

IntelligenceGermanyWeissach,

Bietigheim-Bissingen,

Leipzig,

Mönsheim,WolfsburgPorscheEngineering

US

is

integrated

into

an

international

networkof

competence

centers

focusing

on

digital

technologies.•

Headquarters

oftheInternational

Group

at

thePorsche

R&D

Center

Weissach•Vehicle

technologies

and

integration•

Function

and

softwaredevelopment

incl.software

architectures•Big

Data

and

Artificial

Intelligence•

Testing

andsimulationCzechRepublicPrag,

Ostrava•Vehicle

testing

and

development•

Driver

assistance

systemsand

highly

automated

drivingfunctions•V2X

testing•

E-mobility•

Endurance

testing

and

quality•

Driving

dynamics•

Connectionof

virtual

and

real

testingRomaniaCluj,

TimișoaraItalyNardò

Technical

Center•

China-specific

solutions•

Function

and

software

development•

Connectivity•

Quality•Testing

andsimulationChinaShanghai,

PekingUSALos

Angeles•

Market-specific

vehicle

functions•

Digital

services•

Connectivity

andinfotainment3FOCUSHERITAGELOCATIONSEMPLOYEES100%

subsidiary

of

Dr.Ing.h.c.F.Porsche

AGFoundedin1931byFerdinandPorsche

inStuttgartPEUS

as

Part

of

Porsche

Engineering

Group:

Company

OverviewinGermany,

Italy,

CzechRepublic,Romania,China

andUSA.Technologies

for

theintelligent

and

connectedvehicles

of

the

future1.700EmployeesPEG

ManagementDigitaltechnologiesMarkus-Christian

Eberl(CEO)VehicleexpertiseDirkPhilipp(CFO/COO)DirkLappe(CTO)13Locations4High-voltage

system

development

and

e-mobilityEnd-2-end

electronics

architectureChassis

system

developmentFunction

development

and

testingComplete

vehicle

development

Cayenne

CoupéAI

in

engineeringOur

Experience

in

theField

of

Vehicle

and

System

DevelopmentKnows

Many

Examples.5Porsche

Engineering

x

Sebastian

SteudtnerCommercial

vehicle

developmentX-BOW

with

dual-clutch

transmissionHigh-voltage

system

development

919

HybridChassisDemonstratorBattery

system

development

SeabobDesign

and

styling

of

Material

HandlingPorsche

Engineering

Group

(PEG)Projects6No

Matter

What

We

Develop,

We

Always

Keep

anEye

on

theIntegration

Throughout

The

Complete

Vehicle.

Quality

Included.Vehiclebody

systems

and

functionsE-mobility

and

high-

voltage

systemsE/E

architecture

ASPICE|

ISO26262|

ISO9001|

TISAXIntelligenceSimulation

and

testingChassis

systems

and

functionsComplete

vehicleintegrationHighly

automated

drivingDrive

systems

and

functionsDevelopmentConceptSeriesConnectivityArtificial7PROBLEM

DESCRIP

TIONController-centric

workflows→Longiteration

cyclesOffline

recordings→Hand-operated

dataoffload

(USB/loggers)Manual

measurement

setup

&

configuration→High

effort,

lowrepeatabilityFleet

testing

at

scale→Hard

tomanage,hard

to

compare

resultsResearch

ideas

stayed

long

in

lab

environments→No

finalConfidencebecause

of

missing

field

testsTransforming

from

lab

to

realworld

evaluation

in

the

vehicle

throughour

Cloud

Connected

Realtime

EcosystemWe

smartly

use

our

ecosystem

to

reduce

the

‘Time

To

Car'△8AI-enabledEdgeDeviceStandard

Vehicle

Architecture01

02

03

ANALYZEDeployOverview

of

Our

DevelopmentLoop EXECUTE&COLLECTEVALUATEOPTIMIZE

Cloud

Processing

/

Data

Center

DATA

LAKE

>

NEURAL

NETUpload9Edge

DevicesCloud

ResourcesOn-premise

ResourcesCentralizedDataLakeExperiment

Tracking&ModelRegistryScalableProcessingPlatformIoTEdgePlatform

withFleetMonitoringTechnical

Components

in

theDevelopmentLoopTest

Vehicleequipped

withadditional

EdgeDeviceCapabilitiesCloud-connected

VehicleMiddlewareNVIDIADGX

SystemswithShardingCapabilitiesWorkloadManagementTRAINEDMODELS&

DATASensor

Data&

Model

ResultsAzureIoTHubAzureMODELS10Start

at

the

Vehicle:

the

Edge

Device

as

the

Data

SourceDevices12CustomHardwarePlatformpoweredbyNVIDIA

JetsonHardware

The

CarDataBox

Serves

as

Edge

Device

in

OurAI

Ecosystem.ApplicationLayerConnected

VehicleMiddlewareSoftware13CoreRuntimeCANInterpreterFlexRayInterpreterAutomotiveEthernetCameraInterpreterConfigurationManagementApplicationManager

Custom

HardwarePlatform(NVIDIA

Jetson)Technical

Structure

of

the

CarDataBoxCore

SoftwareLayerApplicationLayerDockerConnected

Vehicle

MiddlewareHardware

LayerROSinterfaceROSinterfaceROSinterfaceMachineLearningComponentAzureUploader

ComponentReal-Time

ApplicationCore.

.

.

.

.14Remote

Application

Deployment

on

Edge

DevicesEngineerContainerRegistry

DEVELOP

&

DEPLOYEXECUTE

&

COLLECTANALYZE

&

OPTIMIZEAnalytics-,ML-

and

Processing-PipelinesRemoteDeploymentWeight

UpdatesDEVICEEvaluation

AppADAS

App▲ML

AppDataLakeAzureEDGE15Cloud

ResourcesOn-premise

ResourcesEdge

DevicesTest

Vehicleequipped

withadditional

EdgeDeviceCapabilitiesCloud-connected

VehicleMiddlewareCentralizedDataLakeExperiment

Tracking&ModelRegistryScalableProcessingPlatformIoTEdgePlatform

withFleetMonitoringTechnical

Components

in

theDevelopmentLoopNVIDIADGX

SystemswithShardingCapabilitiesWorkloadManagementTRAINEDMODELS&

DATASensor

Data&

Model

ResultsMODELS16Technical

Componentsin

theDevelopment

LoopAzureIoT

HubAzureDockerResources18Kubernetes

SpacesProject

space1Project

space

2Project

space

3Data

LakeIndexed

by•

Vehicle

signals•

Vehicle

derivate•

Locations•

Weather

•

…FleetAnalyticsSmallModel

Training

andModel

InferenceProcessing

the

CollectedData

in

the

CloudAzure

IoT

HubAzureNVIDIA

B200

on-premise>LargeModel

TrainingCurated

Datasets19Cloud

ResourcesOn-premise

ResourcesEdge

DevicesTest

Vehicleequipped

withadditional

EdgeDeviceCapabilitiesCloud-connected

VehicleMiddlewareCentralizedDataLakeExperiment

Tracking&ModelRegistryScalableProcessingPlatformIoTEdgePlatform

withFleetMonitoringTechnical

Components

in

theDevelopmentLoopNVIDIADGX

SystemswithShardingCapabilitiesWorkloadManagementTRAINEDMODELS&

DATASensor

Data&

Model

ResultsMODELS20Technical

componentsin

the

development

loopon-premiseResources22LocalInferenceContainerizationPartitioningof

GPUs

tosuit

workloadsDocker

/NVIDIA

ContainerRuntimeMiGPartitioningModel

deployment,

experiment

trackingLLMEvaluation/

MonitoringNVIDIA

DGX

PlatformvLLM

/

OllamaLLMInferencePoCServices

Model

TrainingDeepLearning

Engineers

DGX

Cluster

(B200)AgentDevelopersWorkloadScheduling&Model

Training

NIMTools

/

ProductsSLURM+PyTorch23Anend-to-end,consistentecosystemspanning

data

collection,

real-time

validation,scalable

training,and

edge

deploymentVehiclearchitecture-independentsoftwaredevelopmentenabling

flexibleadaptation

acrossplatformsFleet-scaleoperation,evaluation,and

data

collectionDirectin-vehicle

evaluation&Updated

withinminutesUnifiedglobal

AImodellifecycle,experiment

tracking,

&

governanceEfficiency

GainsFrom

our

Cloud

&

AI

Ecosystem>>>>>Azure

IoT

HubAzureDockercop

ik24Improving

vehiclepositioningaccuracy

duringhigh

dynamicdrivingAI-Agent

driven

vehicledata

analysisUSE

CASE

#1Signal

Foundation

ModelAI-based

scenario,patternand

anomaly

finderApplying

our

Ecosystem

toReal-World

ChallengesUSE

CASE

#3Vehicle

Data

AnalysisUSE

CASE

#2Race

GPS25USECASE

#1SignalFoundation

ModelAI-based

scenario,pattern

and

anomaly

finder26SignalFoundation

Modelfor

ADASScenario

Search,

Clustering,

andDescriptionCHA

L

L

E

NG

E•Largeamounts

of

unlabeled

signalrecordings•Rare/safety-critical

events

arehard

to

find,

compare,

and

quantify

across

fleetsAP

P

ROACHClustering

existing

databaseof

signalrecordingsinto

searchable

database

of

scenariosI

N

P

UTDriving

scenarios

as

time

series

dataO

UTP

UTSearchablelatentspaceLatentspace27Applying

Our

EcosystemDEVELOP

&

DEPLOY

EXECUTE

&

COLLECT

ANALYZE

&

OPTIMIZE•

LaneDetections•

ObjectDetection•

Acceleration/Speed

•

…Weight

UpdatesEngineerAnalytics-,ML-

and

Processing-PipelinesSignals

RecordedAzureDataLake28Description•

Extract

theimportant

featuresof

thescenario•

Representitby

aset

of

vectors

called

“embeddings”•

Embeddingis

acollection

ofcoordinatesinamultidimensionalspace•

Similar

scenarios

are

residingin

the

same

neighborhoodin

this

“Latentspace”Use

Cases

κ•

Similaritysearchingusing

embeddings•

Generatesimilar

scenarios

using

embeddings•

Similaritysearchusing

text•

ForecastingclassificationEmbedding*

Description**

[label]

A

car

at

a

mediumdistancein

themiddlelane

…Self-Attention(GroupedMulti-Query

Attention)

withKVCacheαliSignalFoundation

ModelTime

Series

DataTime

Series

“Foundation

Model”Large

Language

Model*Embedding–

thecoordinatesof

asinglepointinthelatentspace|**Description–

thelabelof

thecurrentscenariorepresentedby

the

embeddingFeedForwardSwiGLURMSNorm Softmax

Linear

RMSNorm

Domaininput

signalsEncoder

DecoderEmbeddingsLatentspaceLatentSpaceDistribution

RMSNorm

νµσ2Sample29SignalFoundation

ModelInitial

application

to

ADASGenerate

Scenesgiven

an

encoded

scene,generate

signals

for

similar

situationsSearch

Scenesgiven

aprovided

scene,search

for

similar

ones

in

a

databaseDescribe

Scenegiven

anencoded

scenario,

describein

natural

language30SignalFoundation

ModelBenefitsClosed-loop

improvement

from

deployment

to

optimizationTurn

rawrecordingsinto

a

searchable

scenario

databaseFaster

root-cause

analysisthrough

similarity

searchGenerate

scenario

variants

to

expand

test

coverage31USECASE

#2

AI-Corrected

Race

GPSforhigh-dynamic

driving32Track

from

Standard

GPSTrack

from

HighPrecision

GPSGO

A

L•Predict

andcorrect

GPSposition

error

directly

from

vehicle

sensor

datausingmachine

learning•

Achieve

accuracy

close

tohigh-precisionDGPSCHA

L

L

E

NG

E•Road

car

GPSnoise:

5-10muncertainty•Navigation

systemsleveragemapmatching&plausibility

checksHigh

Dynamic

Driving

Reduces

GPS

Accuracy•ClassicalGPScorrections

failinhigh

dynamic

driving

situations33SolutionLSTM

toincreaseGPS

accuracy

based

on

series

signalsDEVELOP

&

DEPLOY

EXECUTE

&

COLLECT

ANALYZE

&

OPTIMIZEEngineerSeries

Signals

Recorded•

High-precisionGPS•

Steering

/

Yaw

Angle•

Acceleration

/Deceleration•

IMU(InertialMeasurementUnit)

EDGE

DEVICEAnalytics-,ML-

and

Processing-PipelinesAzureWeightUpdates▲DataLake34Description•

AIbased

SensorFusion

toimprove

accuracybasedon

dynamic

features•

Considers

timehistory

of

vehicle

trajectory•

Targetslowresourceconsumption

to

allowin

vehicleprocessingGPS

Accuracy

ModelLSTMModelGPS-specificlossfunctionAI-Corrected

GPSHighPrecisionGPSINPUT

OUTPUTStandard

GPS35Track

from

Standard

GPSTrack

from

HighPrecision

GPSAI-corrected

GPSPositionAccuracy

boost:Achieves

a~95%

error

reductionGPS5–10m

→

~1–2m

(~1.4

m

RMSE)

Real-world

proof:Validatedon

differentcars(e.g.,

Porsche

911)

→

track-qualityracingline&lap

timingBasedon

standard

vehiclebus

signals

high-precisionGPS

ground

truth(trainingonDGX,

edge-ready)Benefits:

Increased

GPS

Accuracy

Through

LSTMl·36USECASE

#3Decoding

the

FleetAI

Agent-Driven

VehicleData

Analysis37AP

P

ROACH•

Anomaly

detection

on

adiagnosisprotocol

level•LargeProtocol

Transformer-Decoder

ondevelopment

fleet

protocols•Highlevel

of

automation

withmodular

AI

workflowsAI-driven

DevelopmentFleet

Insights

Reduce

Time

to

ReactionCHA

L

L

E

NG

E•Largeamounts

andmanymodalities

of

vehicle

data•

Ambiguousrelationshipsbetween

diagnosisinformation•High

efforts

arounddomain

specific

analyses38Our

solutions

integrate

into

the

development

dataloopand

enable

early

diagnosis.Low-codeworkflowappCustomizableanalysisinsightsIssueresolution

/development

measuresMl-driven

exploration

&

detectionData

feedbackloopsTimeseries

anomaly

detectionDevelopment

fleet

vehiclesAgentic

AI

AnalysisDatalake39Multi

AgentExpert

Process

Provides

Granular

Diagnosis

InsightsADAS

agentChassisagentBattery

agent…Rootcausediagnosticinsights

/