2026汽车人工智能应用十大案例

2026汽车人工智能应用十大案例积极响应“人工智能+”行动，推动人工智能与汽车产业深度融合与应用ActivelyRespondtothe“AIPlus”Initiative,andPromoteDeepIntegrationandApplicationofAlintheAutomotiveIndustry华人民共和国2025年，国务院印发《关于深入实施“人工智能+”行动的意见》,提出推动人工智能与经济社会各行业各领域广泛深度融合。为积极响应“人工智能+”行动，进一步推动人工智能与汽车驾驶自动化、汽车座舱智能化、汽车研发生产智能化深度融合与应用，开展2026汽车人工智能应用十大案例评选工作。评选原用实效性内容完整性用实效性内容完整性案例创新性技术可行性应CaseInnovativenessTechnicalFeasibilityApplicatio汽车驾驶自动化、汽车座舱智能化、汽车研发生产数字化领域的人工智能应用案例确定十大案例发布材料编制确定十大案例发布材料编制公开征集材料核查、补充专家评审高校和科研机构Enterprises智能网联汽车产业创新联盟面向行业公开进行案例征集科技企业自动驾驶企业MaterialsVerificationExpertsReviewandSupplementationTechnologyEnterprisesMaterialsVerificationExpertsReviewandSupplementation方案合规性2026汽车人工智能应用十大案例案例一：蔚来世界模型NWMCase1:NIOWorldModel案例二：小鹏第二代VLA(VLA2.0)Case2:XPENGVLA2.0Case3:WeRideWITTandWeRideGENESISPowerthePhysicalAlFlywheel案例四：基于XLA认知大模型的小米端到端智能辅助驾驶系统Case4:XiaomiHADsystembasedonXLACognitiveLargeModelCase5:All-NewLixiangTongxueSystem-LevelLivisAgent上海蔚来汽车有限公司广州小鹏汽车科技有限公司北京文远知行智能科技有限公司小米汽车科技有限公司北京理想汽车有限公司案例六：思必驰超级天琴2.0Case6:AISpeechSuperorphiVoiceAssistant2.0Case7:NIOSkySight&SkyDetectVehicleAlQualityInspectionSystems案例八：Al数字员工协作平台Case8:AnAIDigitalEmployeeMulti-AgentCollaborationPlatform案例九：汽车研发知识大脑Case9:AutomotiveR&DKnowledgeBrain案例十：制造现场智能感知与决策平台Case10:IntelligentPerception&Decision-MakingPlatformforManufacturing思必驰科技股份有限公司蔚来汽车科技(安徽)有限公司比亚迪汽车工业有限公司东风汽车集团有限公司研发总院上海理想汽车科技有限公司·世界模型+监督微调+闭环强化学习三层训练框架。·世界模型+监督微调+闭环强化学习三层训练框架。·多场景预测方法结合强化学习，实现多策略最优规划。·端到端模型具有预测未来能力、自带安全性理解。Innovation·Three-layertrainingframeworkconsistingofworldmodel,supervisedfine-tuning,andclosed-loopreinforcementlearning;·Innovativelyadoptingamulti-scenariopredictionmethodtoachieveoptimalplanningwithmultiplestrategies;·Theend-to-endmodelpossessestheabilitytopredictthefutureandcomeswithinherentunderstandingofsecurity.应用效果·加速产品的研发迭代，提升智能驾驶安全性。·NWMhasachievedlarge-scaleapplicationacrossvehiclesfrom·Accelerateproductdevelopmentanditeration,andenhancethesafetyofintelligentdriving.·Itcanempowermultipleindustrialfieldsandhelpusmovetowardsgeneralartificialintelligence.··NWM是多元自回归生成式模型，对数据全量理解后可以重建物理世界，具有长时序推演和决策能力，从万千推演中寻找最优解。·NWMisamultimodalautoregressivegenerativemodel.Afterfullyunderstandingthedata,itcanreconstructthephysicalworldandhaslong-termtemporalreasoninganddecision-makingcapabilities,findingtheoptimalsolutionfromcountlessreasoning案例中的数据均来自于企业提供，仅供参考案例中的数据均来自于企业提供，仅供参考案例二：小鹏第二代VLA(VLA2.0)模仿学习模仿学习强化学习创新性·采用创新架构，剔除语言转译冗余链路。创新性·采用创新架构，剔除语言转译冗余链路。Innovation·VLA2.0adoptsaninnovativearchitecturethatthateliminatesredundantlanguage-translationpathways.co-design,achievingbreakthroughsincausalreasoninginthephysicalworldandovercomingthelimitationsoftraditional应用效果·VLA2.0推送首月，辅助驾驶里程占比突破50%,复杂场景处理更稳定顺滑，避让舒适性大幅提升，在安心舒适·Initsfirstmonthafterrollout,VLA2.0pushedtheshareofassisted-drivingmileagepast50%.Ithandlescomplexscenarioswithgreaterstabilityandsmoothness,deliverssignificantlymorecomfortableavoidancemaneuvers,,anddelivershighertrafficefficiencywhilekeepingtheridereassuringandcomfortable.VLA2.0hasnowtraveledacrossmorethan369cities.路线。以视觉为核心的多模态大模型，直接学习真实物·VLA2.0adoptstheapproachof"end-to-end+purevision+worldmodel+self-evolutionviareinforcementlearning",utilizingamultimodallargemodelwithvisionasitscoretolearndirectlyfromtherealphysicalworld.Byremovingthelanguagemediationstep,itachieveshigherinferenceefficiency.应用效果应用效果现约200倍、Token成本降低约98%;在虚拟验证端，·Ontherealdataprocessingside,WITTachievesamaximumtimesandareductionintokencostofabout98%;onthevirtualimprovementindataprocessingefficiencyofapproximately200·将面向真实世界理解的WITT与面向虚拟世界生成的Innovation·Integratingthereal-world-understanding-orientedWITTandthevirtual-world-generation-orientedGENESISintoaunifiedphysicalAlsystem,enablingbidirectionallinkagebetweenreal-worldproblemdiscoveryandvirtual-worldtrainingandverification.心构建双模型物理AI体系，形成“真实数据理解-虚拟场景生成-模型训练优化-车辆应用反馈”的物理AI飞轮。·Buildadual-modelphysicalAlsystemcenteredaroundtheWITTphysicalAlcognitivefoundationmodelandtheGENESISworldmodel,formingaphysicalAlflywheelof"realdataunderstanding-virtualscenegeneration-modeltrainingoptimization-vehicleapplicationfeedback”.案例四：基于XLA认知大模型的小米端到端智能辅助驾驶系统Case4:XiaomiHADsystembasedonXLACognitiveLargeModel创新性创新性·具身基座大模型+潜空间推理实现技术优化。·创新性融合自动驾驶与机器人具身智能训练逻辑。Innovation·TheXLAcognitivearchitectureexpandsthecapabilityboundariesoftraditionalVLAtechnology.·Optimizationoftechnologythroughtheintegrationofalargeembodiedbasemodelandlatentspacereasoning.·Innovativelyintegrateautonomousdrivingwithroboticembodiedintelligencetraininglogic.应用效果助总里程超7亿公里。对比XLA上线前后，可通过多模态信息·XiaomiHAD,builtupontheXLACognitiveLargeModel,hasenteredmassproductionandisdeployedontheXiaomiSU7,YU7andSkynomadvehiclemodels.AsofAugust2026,thecumulativedrivingmileageoftheHADdrivingkilometers.ComparedwiththeperformancebeforeandafterthelaunchofXLA,thesystemleveragesmultimodalinputandspatialreasoningtoenablesafe小米XLA认知大模型：更多模态、更高效、更可控·BasedontheXLAcognitivelargemodelarchitecture,theMiMo-Embodiedlargemodelservesasthecoreengine,nativelysupportingtheorganicintegrationofmultiplesourcesofdata,therebyenhancingthesystem'scognitionandunderstandingofcomplextrafficscenarios.创新性·将车载交互从“功能调用型助手”升级为“座舱系创新性·将车载交互从“功能调用型助手”升级为“座舱系智能体”,面向真实出行中的连续任务与动态约束，形Innovation·Upgradein-vehicleinteractionfroma"function-callingassistant"toa"cockpitsystem-levelintelligentagent",aimingatcontinuoustasksanddynamicconstraintsinreal-lifetravel,andformaninteractionmodewithgreaterinitiative,collaboration,andscenarioadaptability.·复杂任务执行能力提升70%+。·Theabilitytoexecutecomplextaskshasbeenimprovedbyover70%.·Shortenthecompletionprocessofcomplextasksandreducetheoperationalburdenonusers.·Enhancetheefficiencyofmulti-systemcoordinationandconsistencyofinteractionexperiencewithinthevehicle.·采用“主动感知-意图理解-任务规划-跨域调度-状态跟踪-闭环优化”的系统级Agent技术方案，通过任务拆解、约束处理、优先级排序与依赖分析形成执行计划。·Adoptingasystem-levelAgenttechnologysolutionencompassing“activeperception—intentionunderstanding—taskplanning一cross-domainscheduling-tracking-closed-loopoptimization”,anexecutionplanisformedthroughtaskdecomposition,constrainthandling,prioritysorting,anddependencyanalysis.案例六：思必驰超级天琴2.0Case6:AISpeechSuperOrphiVoiceAssistant2.0应用效果·适配不同车型、算力平台及国内外生态，完成40余种语言适配。支持智能补全、纠错、拼接、等待、多轮上下应用效果·适配不同车型、算力平台及国内外生态，完成40余种语言适配。支持智能补全、纠错、拼接、等待、多轮上下·Thesolutionsupportsdiversevehiclemodels,computingsupportfor40+languages.Itsupportsintelligentcompletion,errorcorrection,utteranceconcatenation,adaptivewaithandling,multi-turncontextunderstanding,multi-intentrecognitionfromaplatforms,anddomesticandinternationalecosystems,withsingleutterance,andmulti-persona,emoti创新性·将中枢大模型、分布式智能体、端侧模型和车机物模型Innovation·Unifytheorchestrationofthecentralfoundationmodel,distributedAlagents,on-devicemodels,andin-vehicleobject·ThesolutionintegratesandjointlyoptimizesASR,NLU,DM,TTS,andmultimodalsentimentrecognition,toenablemorenatural,context-aware,andemotionallyexpressiveinteractions.models,combiningtheadvancedreasoningcapabilitiesoflarge·以端云协同“1+N”分布式智能体为底座，融合端到端情感化多模态交互、长短时记忆与分布式智能体等技术，实现超拟人对话、全场景自由说、主动智能服务。·Builtonacloud-edgecollaborative“1+N”distributedAlagentarchitecture,thesolutionintegratesend-to-endemotionallyintelligentmultimodalinteraction,long-andshort-termmemory,anddistributedintelligencetodeliverhighlyhuman-likeconversations,naturalvoiceinteractionacrossallscenarios,andproactiveintelligentservices.案例七：蔚来车边云协同的“天瞳天探”整车AI质检平台Case7:NIOSkySight&SkyDetectVehicleAIQualityInspectionSystems应用效果应用效果·3分钟内实现千余项全车功能自检。较传统人工，检测效率提升10.3倍，检测成本降低11%。·Completesself-inspectionover1,000vehiclefunctionsin3minutes.Comparedtotraditionalmanualmethods,thedetectionefficiency·Scalableacrossautomotiveparts,consumerelectronics,homeappliances,andotherindustries.使车辆从“被检测对象”进化为“自主检测者”。使车辆从“被检测对象”进化为“自主检测者”。身智能体高效协作与精准检测。Innovation·IntegrationofonboardcomputingcapabilitiesandAlalgorithmsintoVehicle-Edge-Cloudcollaborativequalityinspectionsystem,transformingvehiclesfrominspectedtargetsintoautonomousinspectionagents.·CollaborationamongmultipleembodiedAlagentsonproductionlinethroughAlalgorithminnovations.蔚来“天瞳天探”整车Al质检平台TechnicalSolution·TechnicalarchitectureforVehicle-sideautonomousexecution,Edge-basedreal-timeperception,andCloud-basedcoordinated·Full-vehiclefunctionalself-inspectionwithoutexternalequipmentormanualbyintegratingonboardsoftwareandhardwarecapabilities,Edge-basedIntelligentdevices,andCloud-basedAlalgorithms.案例八：AI数字员工协作平台Case8:AnAlDigitalEmployeeMulti-AgentCollaborationPlatform应用效果·开发提效：核心环节效率大幅提升，释放工程师创造力。·风险拦截：质量问题前置拦截，大幅降低返工成本。应用效果·开发提效：核心环节效率大幅提升，释放工程师创造力。·风险拦截：质量问题前置拦截，大幅降低返工成本。·Developmentefficiencyimprovement:Significantefficiencygainsacrosscoreprocesses,unleashingengineers'creativity.·RiskInterception:Qualityissuesarecaughtupfront,dramatically·Automaticclosedloop:Automaticallyscheduletasksandbreakdownbarriersbetweenlinks.将分散工具、孤立知识与AI能力整合为一体化研发智能isolatedknowledge,andAlcapabilitiesintoanintegratedR&Dintelligentagentworkspace.k*Innovation·Multi-agentschedulingalgorithmsfacilitatetaskdistributionandstatesynchronization.·Usingprojectspaceasthedeliveryunit,Agentsreplacemanualcoordinationthroughouttheentireprocess.案例九：汽车研发知识大脑·Focusingonbothexistingandincrementalknowledgeinthefieldofautomotiveresearchanddevelopment,wedesignanAlbrainsolutionforautomot应用效果·企业级知识资产底座。·该知识平台服务数千名研发工程师，研发知识利用成本同比降低20%。·全面助力研发体系智能化升级。·Buildanenterprise-gradeknowledgeassetfoundation.·ServinghundredsofR&Dengineers,cuttingthecostofR&D·DriveintelligentupgradingoftheoverallR&Dsystem.·通过全国产化架构实现AI知识应用的全链路闭环。·与业务共创Al应用矩阵，依托向量知识库与AlAgent,Innovation·Achieveafull-chainclosedloopforAlknowledgeapplicationthroughafullylocalizedarc·CollaboratewithbusinessestocreateanAlapplicationmatrix,adoptinggraph-enhancedmulti-agentschedulingtechnologytobreakdowninformationsilos.案例十：制造现场智能感知与决策平台制造现场智能感知与决策平台技术架构gAgA.感知层◎曲工艺涂装总装·平台采用“感知一决策一执行一反馈”分层架构，总体·平台采用“感知一决策一执行一反馈”分层架构，总体·TheplatformadoptsalayeredPerception→Decision-Making→Execution→Feedbackarchitecture,comprisingthreekeystages:dataacquisition,dataprocessing,andinformationstructuringandpresentation.Itenablesunifiedmodelingacrossdiversemanufacturingprocessesandequipmenttypes.应用效果·Comparedtotraditionaloperationalmethods,theefficiencyofprogramandparameterinspectionhasbeensignificantlyimproved.·Enableddetectionofstatuschanges