查看購物車
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
2074810

人工智慧在駕駛座中的應用（2026 年）

Research Report on AI Applications in Cockpits, 2026
出版日期: | 出版商: ResearchInChina | 英文 390 Pages | 商品交期: 最快1-2個工作天內

價格
簡介目錄

人工智慧在駕駛座中的應用—人工智慧服務變得更加全面、便利和複雜。

2026年上半年，駕駛座人工智慧能力在多個領域經歷了初步升級，從被動響應轉向及時主動的行動，從單一功能轉向服務循環，以及從以雲端為中心轉向以邊緣為中心。隨著智慧體能力的進一步提升，使用者評估駕駛座人工智慧能力的標準也正在改變。關注點不再是哪個模型能達到更高的指標，而是哪個駕駛座人工智慧系統能夠真正執行任務、保護隱私、預測使用者需求並理解使用者要求。

隨著用戶越來越重視實際體驗和有效性，駕駛座人工智慧正在三個主要方向上同時升級：「更全面」、「更便捷」和「更複雜」。

更全面——從「功能」到「系統」

到 2025 年，駕駛座人工智慧將被定位為一項「功能」，專注於各種垂直場景，並能夠根據用戶指示完成一次性服務。

到2026年，主流旗艦車型的駕駛座AI服務將更加「系統化」，透過集中式平台模型和智慧體，整合此前分散的非安全相關AI應用功能，在視聽娛樂、出行輔助、本地生活服務等行業場景中實現封閉回路型服務。部分駕駛座產品甚至能夠基於自主規劃，依序完成多項任務，顯著拓展AI服務場景，提升使用者體驗，並為駕駛座AI的迭代升級奠定基礎。

特別是，人工智慧層面的跨領域整合將為人工智慧服務建立更「全面」的技術基礎，一些原始設備製造商已經在部署方面取得了進展。

例如，ZEEKR 8X 的「超級伊娃」（Super Eva）系統整合了駕駛座和駕駛體驗，並與以武器輔助記憶（WAM）為核心的車輛人工智慧架構相連，支援持續的全模態感知、深度思考和決策，以及全局調度（包括駕駛座、駕駛、底盤、動力傳動系統等的調整）。它也具備自我反思和進化能力，使用次數越多，就越能提升使用者體驗。

在特定場景下，Super Eva 可連接車內和外部生態系統，實現「語音操控」日常任務（例如，直接在車內透過語音指示訂餐、預訂飯店、處理商務資訊等）。此外，它還整合了 G-ASD 4.0，可實現自動駕駛和導航，從「智慧乘客座椅」轉變為「可靠駕駛員」，進一步拓展了 AI 服務場景。

以IM的「IM ULTRA AGENT 1.0」為例。透過IM FUSION NOVA將駕駛座與駕駛功能整合，並採用完全整合的智慧架構，IM的駕駛座AI系統能夠與IM AD ZETA和全線控Lizard數位底盤進行跨域協作，實現諸如駕駛過程中自由更改目的地等功能。此外，該系統還可應用於視聽娛樂、生態系統互聯、個人化互動等非安全場景，從而形成使用者指令分析與服務的閉合迴路。

更方便：少說多做

便利性取決於使用者為達到預期結果所需付出多少努力。

到2026年，使用者將更直接地體驗到人工智慧應用帶來的益處。只要減少文字數量、點擊次數和等待時間一秒，使用者體驗就能提升到新的高度。因此，2026年的駕駛座人工智慧應進一步減少使用者在使用服務時所遇到的摩擦。這意味著更直接的互動模式、更少的操作步驟和更快的回應速度。透過利用高精度語音辨識（ASR）技術、更智慧的人工智慧演算法調度和人性化的工作流程設計，冗餘操作和頁面切換將被最小化，並且可以透過一句話執行多個命令。

例如，我們來考慮這樣一個場景：「您接孩子回家」。

以前，用戶首先需要指定接送孩子的地點「A」，然後說「開始導航」。系統會問「您想去哪裡？」，使用者回答「地點 A，XX 街」，系統才會回覆「路線已規劃」。換句話說，完成一項任務需要三次互動。

目前，用戶只需說「我去接孩子」，人工智慧就會自動輸入目的地，並根據儲存的數據產生路線。這樣，只需一條簡單的語音指令，就能完成三項任務。

例如，東軟OneCoreGo 7.0透過其「一體化」子解決方案設計，提供更全面、更便利的AI服務。其代理協作技術允許透過單一命令執行跨不同使用場景的多階段操作。

實現駕駛座人工智慧便利性的關鍵之一在於實施多智慧體協作的標準協定和統一的調度框架。結合邊緣雲端協作部署環境，標準化的智慧體間通訊、編配和執行協定能夠有效應對跨領域智慧體互通性的挑戰。

Extour Technology 的 MCP-Agent 框架將距離偵測、門市選擇、路線規劃和支付等任務分割成多個獨立的代理商。每個代理程式都透過 MCP 標準協定與其他代理通訊。例如，如果用戶說「我要一杯低脂咖啡」，系統只需幾分鐘即可完成從產品選擇到下單和導航的整個流程。

透過利用上下文視窗最佳化技術和記憶體模組，MCP-Agent 能夠持續追蹤用戶需求的變化，例如咖啡訂購過程中咖啡種類、杯型和取貨地點的變更，而無需用戶重複提供背景資訊。在標準化的服務間通訊協定的支援下，它能夠處理諸如「我30分鐘後到辦公室，請推薦一些低卡路里咖啡」之類的複雜請求，並自動協調電池電量檢測、低卡路里門市篩選和路線規劃等服務。所有任務均可透過一條語音指令完成，省去了傳統解決方案中在多個獨立應用程式之間切換的繁瑣步驟，顯著提升了用戶與人工智慧服務互動的便利性。

相較之下，東軟的NAGIC.AI解決方案也包含子智慧體來處理各種場景。然而，其完整的智慧體協同機制是透過路由器、HCP、記憶體和函數呼叫（工具鏈）等模組的協調實現的。路由器分析使用者的模糊意圖，並指派對應的場景特定智慧體。記憶體共用統一的記憶體池，支援不同智慧體執行意圖。函數調用則與每個智慧體協同工作，調用車輛的底層硬體，包括導航、ADAS、駕駛座IVI和多媒體等功能。

此外，NAGIC.AI 採用「分散式+集中式」解決方案。基於標準化介面和統一推理框架，它能夠分層適合不同的運算平台（高效能晶片/中階平台）和不同的系統（Linux/QNX/AutoSAR）。它還內建嵌入式運算平台 (HCP) 和 AI 插件運作中服務層，為功能模組提供標準化的存取和擴充能力。

進一步完善—深入了解「隱性需求即服務」

駕駛座人工智慧領域的「智慧化」競爭正從三個層面展開：更敏銳的感知、更深刻的理解和更精準的行動。在此背景下，感知使用者的「隱性需求」代表著一項突破性的進步。

使用者在汽車駕駛座內有著各種各樣的需求，從「高效通勤」和「放鬆身心」到「社交互動」不等。因此，有必要識別並滿足不同場景下的這些潛在需求。到2026年，駕駛座人工智慧產品通常會透過感知、記憶、理解、判斷、執行和檢驗等工作流程來處理這些潛在需求。垂直場景已預先配置，並採用特定領域的智慧體來完成相應的操作：

以感知為例，駕駛座人工智慧正開始整合視覺、語音和車輛訊號。在「出行服務」或「兒童保育」等特定場景下，它可以透過感知面部表情、身體動作、眨眼頻率和方向盤握持情況，在乘員發出語音指令之前預測用戶需求，從而在預配置的邏輯框架內實現端到端的主動服務。

OEM廠商應特別注意三種類型的場景特徵：安全場景、舒適場景和習慣場景。

以Modelbest Technology公司的「SuperMate」為例…

Modelbest Technology的駕駛座AI設計理念以「終極隱性理解」取代「功能疊加」，透過深度記憶、即時感知、情境感知和主動行動的封閉回路型，實現「更加克制、謹慎和無意識的服務」。典型功能包括無意識車輛控制、介入兒童危險行為、事故情況識別以及提供情感慰藉。

SuperMate 的許多特點中，最引人注目的是其事故反應場景中採用的「主動式 + 無意識服務」方法。

此外，與其他常見的車載場景功能相比，SenseAuto 和東軟集團都推出了各自獨有的車門開啟警告（DOW）功能。該功能將用戶對車內安全狀況的隱性需求擴展到了車外路況。

例如，SenseAuto 的「安全衛士」智慧體是基於對底層模型的理解，能夠進行多維度的風險識別，對諸如車門開關過程中發生的碰撞以及車輛損壞等事件進行分類和解釋。此外，它還透過基於安全閉合迴路和 OpenClaw 系統的主動預警和即時提醒，使用戶能夠隨時隨地監控車輛的安全狀態，從而確保在各種情況下都能安全駕駛。

目錄

定義

第1章：人工智慧在汽車駕駛座的應用場景

  • 人工智慧在駕駛座中的應用現狀
  • AI駕駛座的特點
  • 駕駛座內人工智慧應用場景：現狀
  • 駕駛座人工智慧的演變
  • 場景一：語音辨識
  • 整合語音辨識功能的AI平台模型開發藍圖
  • 子場景 1：語音辨識
  • 子場景 2：外部語音辨識
  • 語音對話解決方案供應商整合了基於人工智慧的模型。
  • 場景 2：多模態互動
  • 整合臉部辨識功能的AI模型開發藍圖
  • 將小型模型整合到唇部運動識別場景中
  • 將小型模型整合到虹膜辨識場景中
  • 場景 3：IMS
  • 實施飛行中監控系統功能
  • 開發用於飛行中監視場景的人工智慧
  • 用於飛行中監視的人工智慧演算法範例
  • 車載監控：晶片供應商應用人工智慧技術
  • 場景 4：HUD
  • 人工智慧演算法在HUD的應用
  • 場景五：雷達偵測
  • 雷達中的人工智慧演算法

第2章：駕駛座人工智慧應用現況與發展趨勢

  • 駕駛座人工智慧市場數據
  • 2025 年人工智慧語音助理的安裝數量及其普及率
  • 按價格區間分類的AI語音助理滲透率（2025年）
  • 人工智慧語音助理普及率（按級別分類（2025 年））
  • 按新能源汽車類型分類的人工智慧語音助理普及率（2025 年）
  • Avatar 安裝量與採用率（2025 年）
  • 按價格範圍分類的虛擬形象採用率（2025 年）
  • 頭像採用率（依等級分類（2025 年））
  • 按新能源汽車類型分類的虛擬化身採用率（2025 年）
  • 人工智慧模型的採用現狀和滲透率（2025 年）
  • 按價格區間分類的AI模型採用率（2025年）
  • 人工智慧模型的採用率（按級別分類（2025 年））
  • 按新能源汽車類型分類的人工智慧平台模型採用率（2025 年）
  • 駕駛座人工智慧的發展趨勢
  • 趨勢 1：駕駛座助手正在從聊天機器人擴展到代理商。
  • 趨勢 1：駕駛座代理應用程式密鑰
  • 趨勢二：基於整合代理平台的駕駛座與駕駛座椅的整合
  • 面向OEM廠商的跨領域整合AI超級代理佈局（2026年）
  • 趨勢
  • 駕駛座引入人工智慧技術的資源運算
  • 資源計算（僅供參考）
  • 主要國際品牌駕駛座人工智慧助理應用程式對比
  • 各種駕駛座人工智慧演算法的優缺點

第3章：供應商提供的駕駛座人工智慧應用案例研究

  • 供應商提供的駕駛座人工智慧平台模型概述
  • Huawei
  • Tencent
  • Alibaba
  • Baidu
  • Zhipu AI
  • SenseTime
  • iFLYTEK
  • AISpeech
  • Unisound
  • Pachira
  • Thundersoft
  • Neusoft
  • Desay SV
  • TINNOVE
  • Cerence
  • 其他
  • MINIEYE
  • LG
  • MediaTek

第4章：不同汽車製造商的駕駛座人工智慧應用實例

  • 面向原始設備製造商的基礎模型應用
  • NIO
  • Li Auto
  • Lixiang Tongxue
  • Xpeng
  • Xiaomi
  • BYD
  • Geely
  • 星睿AI平台模型
  • 星睿人工智慧基金會模型架構
  • ZEEKR
  • Chery
  • Changan
  • Great Wall Motor
  • SAIC
  • GAC
  • BAIC
  • FAW
  • JAC
  • Tesla
  • BMW
  • 其他
簡介目錄
Product Code: GX023

AI Application in Cockpits: AI Services Become More Comprehensive, Convenient, and Refined.

In the first half of 2026, cockpit AI functions underwent initial upgrades across multiple dimensions, including from passive response to timely proactive action, from single-point functions to service loops, and from cloud-centric to edge-centric approaches. As agent functions are further enhanced, users' evaluation criteria for cockpit AI capabilities are also changing: the focus is no longer on which model achieves more advanced metrics, but rather on whose cockpit AI system can truly perform its tasks, protect privacy, anticipate user needs, and understand user demand.

Users' emphasis on actual experience and effectiveness is forcing cockpit AI to upgrade along three major lines simultaneously: more comprehensive, more convenient, and more refined.

More Comprehensive: From "Function" to "System"

In 2025, cockpit AI was positioned as a "function," focusing on different vertical scenarios and capable of completing single-shot services based on user instructions.

In 2026, the cockpit AI services of mainstream flagship vehicle models become more "systematic" and support the coordinated use of originally scattered non-safety AI application functions through central foundation models/agents to achieve a closed-loop service in vertical scenarios such as audio-visual entertainment, itinerary assistance, and local life services. Some cockpit products can even complete multiple tasks step by step based on independent planning, greatly broadening the scope of AI service scenarios, improving user experience, and laying the foundation for iterative upgrades of cockpit AI.

Especially, cross-domain integration at the AI level marks a more "comprehensive" technology foundation for AI services, and the layout of some OEMs has already been implemented:

For example, ZEEKR 8X's Super Eva fitted with cockpit-driving integration connects to the vehicle AI architecture centered on a WAM, supporting functions such as full-time, all-modal perception, deep thinking and decision-making, and full-domain scheduling (coordinating cockpit, driving, chassis, powertrain, etc.). It can also self-reflect and evolve, becoming more user-friendly with use.

In scenarios, Super Eva not only connects the in-vehicle and external ecosystems, enabling "speak-and-handle" daily tasks (e.g., ordering food, booking hotels, and processing work information directly via voice in the car), but also collaborates with G-ASD 4.0 to achieve autonomous driving and navigation, transforming from a "smart front passenger" into a "reliable driver," further expanding the scope of AI service scenarios.

Take IM's IM ULTRA AGENT 1.0 as an example. Through the IM FUSION NOVA cockpit-driving integration full-domain fusion intelligent architecture, the IM cockpit AI system allows for cross-domain linkage with IM AD ZETA and the fully wire-controlled Lizard Digital Chassis to realize functions such as changing destinations at will on the way. It can also be implemented in non-safety scenarios such as audio-visual entertainment, ecosystem interconnection, and personalized interaction to complete user command analysis and service closed loop.

More Convenient: Less Talk, More Action

Convenience hinges on how much effort users take for their desired outcome.

In 2026, users can feel the effects of AI applications more directly: with one less word, one less click, and one less second of waiting, the experience can reach a higher level. Therefore, cockpit AI in 2026 should further reduce the friction users encounter when accessing services: more direct interaction modes, fewer interaction steps and faster response speeds. Leveraging high-precision speech ASR technology, smarter AI algorithm scheduling and more human-centric workflow design, it minimizes redundant operations and page jumps, enabling multiple commands to be fulfilled with a single sentence.

Take the "picking up kid and navigating the way home" scenario for example:

Past: The user first clarifies the location A for picking up kid, then says "Start navigation"; the system asks "Where would you like to go?", the user replies "Location A, XX Road", and the system responds "Route planned for you", which means three dialogue turns are needed to complete one single task.

Now: The user simply says "Pick up kid", and the AI automatically fills in the destination and generates a route based on data stored in its memory, allowing three tasks to be completed via one vague voice command.

For example, Neusoft OneCoreGo 7.0 provides more comprehensive and convenient AI services through a "multi-in-one" sub-solution design. Multi-step operations of different application scenario functions can all be implemented by a single command through cross-agent collaboration technology.

One of the keys to realizing the convenience of cockpit AI is to implement multi-agent collaboration standard protocols and a unified scheduling framework; paired with an edge-cloud collaborative deployment environment, standardized agent communication, orchestration and execution protocols address interoperability challenges across cross-domain agents.

Extour Technology's MCP-Agent framework splits range detection, merchant screening, route planning, payment, etc. into separate agents. Different agents collaborate with each other through the MCP standardized protocol - for example, if a user says "order a low-fat coffee", the system can run through the entire link from product selection to ordering to navigation in a few minutes.

Leveraging context window optimization technology and memory modules, the MCP-Agent can continuously track successive changes to user requirements, such as adjusting coffee selections, cup sizes and pickup addresses during a coffee order, without requiring users to restate background information. Supported by standardized protocols for cross-service collaboration, it can process complex requests like "I will arrive at the office in half an hour, please recommend several low-calorie coffees" by automatically linking battery range detection, low-calorie merchant filtering, route planning and other services. All tasks can be completed with a single voice command, eliminating the cumbersome operation of switching between multiple independent applications in traditional solutions and significantly boosting the convenience of AI service interaction for users.

In contrast, Neusoft's NAGIC.AI solution also includes sub-agents for different scenarios. However, the complete multi-agent collaboration mechanism is achieved through the collaboration of modules such as Router, HCP, Memory, and Function Call (toolchain). The Router parses users' ambiguous intentions and dispatches corresponding scenario-specific agents. The Memory shares a unified memory pool to realize intention completion across different agents. Afterwards, the Function Call works with each agent to invoke underlying vehicle hardware, including navigation, ADAS, cockpit IVI, multimedia and other functions.

Furthermore, NAGIC.AI adopts a "distributed + centralized" solution. Based on standardized interfaces and a unified inference framework, it achieves layered adaptation to different computing power platforms (high-performance chips/mid-range platforms) and different systems (Linux/QNX/AutoSAR). It also includes built-in HCP (Heterogeneous Computing Platform) and AI Plugin Service Layer, providing standardized access and expansion capabilities for functional modules.

More Refined: Insight into "Implicit Demand as a Service"

The competition for the "refinement" of cockpit AI is unfolding from three levels - sharper perception, better understanding, and more measured actions. Wherein, sensing users' "implicit needs" is one of breakthroughs.

Users have diverse demand inside vehicle cockpits, ranging from "efficient commuting" and "relaxing" to "social interaction". Implicit needs in various scenarios need to be identified and fulfilled. In 2026, cockpit AI products typically process these implicit needs through a workflow consisting of perception, memory, comprehension, judgment, execution and verification. Vertical scenarios are pre-configured, and domain-specific agents are adopted to complete corresponding operations:

Taking perception as an example, cockpit AI is beginning to integrate vision, audio and vehicle signals. In limited scenarios such as "mobility services" and "child care", it can predict user needs in advance by sensing the occupant's expressions, body movements, blink frequency, steering wheel posture, etc., before the occupant issues voice commands, and provide end-to-end proactive services within a preset logical framework.

There are three types of scenario functions that OEMs may pay extra attention to, namely safety scenario functions, comfort scenario functions, and habit scenario functions:

Take Modelbest Technology's "SuperMate" as an example:

Modelbest Technology's design concept for cockpit AI is to replace "superposition of functions" with "extreme tacit understanding", and achieve "more restrained and restrained senselessness" through a closed loop of deep memory, real-time perception, situational understanding and proactive action. Typical functions include senseless car control, intervention of children's dangerous behaviors, accident status recognition and emotional comfort, etc.

Wherein, the most distinctive feature is the "active + senseless service" in the accident handling scenario of "SuperMate":

In addition, compared to other common in-cabin scenario functions, both SenseAuto and Neusoft Group have launched distinctive door open warning (DOW) functions. Such capabilities extend users' implicit safety needs beyond the cockpit to external road conditions.

For example, the "Safety Guardian" agent of SenseAuto, based on understanding capabilities of foundation models, achieves multi-dimensional risk identification, classifies and describes events such as dooring and car scratches, and through a safety closed loop and OpenClaw-based proactive warnings and real-time reminders, allows users to monitor the safety status of their vehicles anytime, anywhere, protecting their all-scenario driving safety.

Table of Contents

Definition

1 AI Application Scenarios in Automotive Cockpits

  • 1.1 Status Quo of AI Applications in Cockpits
  • Characteristics of AI Cockpits
  • AI Application Scenarios in Cockpits: Status Quo
  • Evolution of Cockpit AI
  • 1.2 Scenario 1: Voice Recognition
  • Development Roadmap of AI Foundation Models Integrated with Voice Recognition
  • Sub-scenario 1: Voiceprint Recognition
  • Sub-scenario 2: External Voice Recognition
  • Voice Interaction Suppliers Integrate AI Foundation Models
  • 1.3 Scenario 2: Multimodal Interaction
  • Development Roadmap of AI Foundation Models Integrated with Face Recognition
  • Integration of Small Models in Lip Movement Recognition Scenarios
  • Integration of Small Models in Iris Recognition Scenarios
  • 1.4 Scenario 3: IMS
  • Functional Implementation of In-Cabin Monitoring System
  • Development of AI in In-Cabin Monitoring Scenarios
  • Examples of AI Algorithms for In-Cabin Monitoring
  • In-Cabin Monitoring: AI Technology Applications by Chip Suppliers (1)-(4)
  • 1.5 Scenario 4: HUD
  • Applications of AI Algorithms in HUD
  • 1.6 Scenario 5: Radar Detection
  • AI Algorithms in Radar (1)-(2)

2 Status Quo and Trends of Cockpit AI Applications

  • 2.1 Cockpit AI Market Data
  • Installations and Penetration Rate of AI-powered Voice Assistants, 2025
  • Penetration Rate of AI-powered Voice Assistants by Price Range, 2025
  • Penetration Rate of AI-powered Voice Assistants by Level, 2025
  • Penetration Rate of AI-powered Voice Assistants by New Energy Vehicle Type, 2025
  • Installations and Penetration Rate of Avatars, 2025
  • Penetration Rate of Avatars by Price Range, 2025
  • Penetration Rate of Avatars by Level, 2025
  • Penetration Rate of Avatars by New Energy Vehicle Type, 2025
  • Installations and Penetration Rate of AI Foundation Models, 2025
  • Penetration Rate of AI Foundation Models by Price Range, 2025
  • Penetration Rate of AI Foundation Models by Level, 2025
  • Penetration Rate of AI Foundation Models by New Energy Vehicle Type, 2025
  • 2.2 Development Trends of Cockpit AI
  • Trend 1: Cockpit Assistants Spread from Chatbots to Agents
  • Trend 1: Key to Cockpit Agent Applications (1) - Multimodal Technology
  • Trend 1: Key to Cockpit Agent Applications (2)
  • Trend 1: Key to Cockpit Agent Applications (3)
  • Trend 2: Cockpit-Driving Integration Based on Unified Agent Foundation
  • Cross-Domain Integration AI Super Agent Layout of OEMs in 2026 (1)-(2)
  • Trend 2: Challenges for Cockpit-Driving Integration AI
  • Trend 3:
  • Trend 4:
  • Trend 5:
  • Trend 6:
  • Trend 7:
  • 2.3 Resource Calculation for AI Technology Implementation in Cockpits
  • Resource Calculation (for Reference Only)
  • Comparison between Cockpit AI Assistant Applications of Major Foreign Brands
  • Advantages and Disadvantages of Different Cockpit AI Algorithms

3 Cockpit AI Application Cases of Suppliers

  • Overview of Cockpit AI Foundation Models from Suppliers
  • 3.1 Huawei
  • Planning for AI Applications in Cockpits
  • Functional Construction of HarmonySpace
  • Celia's Voice Capabilities Based on Foundation Models
  • Celia's Perception Capabilities Based on Foundation Models
  • AI Functions of Harmony OS
  • Dynamics of HarmonySpace: MoLA Upgrade
  • Dynamics of HarmonySpace: Special Functions (1)
  • Dynamics of HarmonySpace: Special Functions (2)
  • 3.2 Tencent
  • Cockpit System Upgrade (1): Evolution
  • Cockpit System Upgrade (2): Functions and Architectures
  • Foundation Models-Based Open Agent Platform
  • Cooperative Ecosystem
  • Intelligent Cockpit Foundation Model Framework
  • Foundation Models Enhance Interactive Functions
  • 3.3 Alibaba
  • AI-Based Voice Scenarios
  • NUI Edge-Cloud Integration Platform Architecture
  • Functional Applications of Qwen Edge Foundation Models on IVI
  • Qwen Edge Foundation Models: Edge-Cloud Collaboration
  • Qwen Edge Foundation Models: Terminal Foundation Models
  • Qwen Edge Foundation Models: Application Scenarios
  • Qwen Edge Foundation Models: Application Roadmap of "Human-like Partners"
  • Qwen Edge Foundation Models: Next Steps
  • 3.4 Baidu
  • Intelligent Cockpit Based on Ernie Bot
  • Multi-agent Collaboration Mode
  • 3.5 ByteDance (Volcano Engine)
  • Next-generation Automotive AI Solution
  • Cockpit AI Assistant Construction
  • Cockpit AI Assistant Construction: Four Levels of Edge AI
  • Cockpit AI Assistant Construction: Functional Applications of Edge AI (1)-(8)
  • Cockpit AI Assistant Construction: Hardware Solution
  • Cockpit AI Collaboration: Suppliers
  • Cockpit AI Ecosystem: OEMs
  • 3.6 Zhipu AI
  • Cockpit Design Architecture Based on AI Foundation Models
  • Scenario Design of AI Foundation Models
  • Design of AI Foundation Models for Cockpit Interaction Problems
  • Collaboration in Cockpit AI
  • 3.7 SenseTime
  • Typical Functions of Intelligent Cockpits
  • Cooperative Dynamics:
  • Typical Cockpit Products: Edge Models and AIOS
  • Typical Cockpit Products: Edge Agents
  • Multimodal Processing Capability Framework
  • Multimodal Interaction Application Cases
  • In-Cabin Monitoring Products
  • 3.8 iFLYTEK
  • Functions of Spark Model
  • Core Capabilities of Spark Model
  • Deployment Solution of Spark Model
  • Automotive assistant based on Spark Model
  • Functions of Spark Model: (1)-(6)
  • How Spark Cockpit Integrates AI Services
  • Application Technology of Spark Model
  • Full-Stack Intelligent Interaction Technology
  • Autonomous Vehicle AI Algorithm Chip Compatibility
  • Features of Multimodal Perception System
  • Spark Intelligent Cockpit 2.0
  • 3.9 AISpeech
  • Functions of Automotive Voice Assistant
  • "1+N" Layout of DFM
  • Integrated Foundation Model Solutions
  • Multimodal Interaction Solutions of AI Voice Technology
  • Features of AI Cockpits
  • Cooperation Cases: (1)-(2)
  • 3.10 Unisound
  • Automotive Foundation Model Solutions
  • Details of Foundation Models (1)-(3)
  • Applications of Shanhai Large Model in Cockpits (1)-(3)
  • Business Models of Automotive Voice Solutions
  • Voice Basic Technology
  • Edge-Cloud Collaborative Voice Technology
  • Cooperative Ecosystem
  • 3.11 Pachira
  • Voice Foundation Model Solutions
  • Intelligent Cockpit Foundation Models (Hybrid Architecture + Open Fusion)
  • Applications of DeepSeek
  • AI Voice Solutions (1): Capability Types
  • AI Voice Solutions (2): Functional Scenarios
  • AI Voice Solutions (3): Features
  • AI Voice Solutions (4): Design Concept
  • 3.12 Thundersoft
  • Foundation Model Layout
  • AI Cockpit Series (1)-(3)
  • AI Cockpit Software Foundation - Aqua Drive OS (1)-(2)
  • AI Cockpit Software Foundation - AI Box
  • 3.13 Neusoft
  • Cockpit AI Solutions (1)
  • Cockpit AI Solutions (2)
  • Cockpit AI Solutions (3)
  • Cockpit AI Solutions (4)
  • Cockpit AI Scenarios (1)-(3)
  • Empowering Cross-Domain Integration Platform Products with AI
  • Applications of DeepSeek
  • Underlying Foundation of Cockpit Agents: OS and SOA
  • Next-generation Cockpit Mobility Solutions (1)-(6)
  • 3.14 Desay SV
  • Main Application Scenarios of Cockpit Foundation Models
  • Multimodal Interaction of Cockpit Foundation Models
  • History of Automotive Voice Research
  • Overview of Voice Foundation Model Solutions
  • Voice Industry Solutions (1)-(4)
  • Dynamics in Cockpit AI Cooperation
  • Cockpit Development Trends
  • Upgrade of Smart Solutions
  • 3.15 TINNOVE
  • Four Stages of Intelligent Cockpit Planning
  • AI Cockpit Architecture Design
  • AI large model application scenarios
  • TTi AI Cockpit (1)-(9)
  • 3.16 PATEO CONNECT+
  • Voice interaction technology
  • Capabilities of Qing AI Voice (1)
  • Capabilities of Qing AI Voice (2)
  • Dynamics in AI Cockpit Product Cooperation (1)-(3)
  • 3.17 Extour Technology
  • "1+3" Architecture of Cockpit AI Solutions
  • Edge-Cloud Collaborative Architecture of Cockpit AI Solutions
  • Underlying Architecture of Xinjie AI System
  • Typical Functions of AI System (1)-(4)
  • MCP-Agent Framework Accelerates MAS Intelligent Collaboration
  • 3.18 Cerence
  • Core Voice Technology (1)
  • Core Voice Technology (2)
  • Voice interaction outside the vehicle
  • 3.19 Horizon Robotics
  • Vehicle Intelligent Agentic OS
  • Cockpit-Driving Integration Chip for Vehicle Intelligent Agentic OS
  • 3.20 Others
  • MINIEYE
  • LG
  • MediaTek

4 Cockpit AI Application Cases of OEMs

  • Foundation Model Applications of OEMs
  • 4.1 NIO
  • Multimodal Perception Foundation Models: NOMI GPT
  • Multimodal Cockpit Interaction Applications Based on NOMI GPT
  • Applications of NOMI GPT in Cockpits
  • Intelligent Cockpit Functions Based on NOMI GPT (1)-(5)
  • ONVO Intelligent Cockpit Interaction Cases Based on NOMI GPT (1)-(2)
  • OnVO and NIO Offer Food Ordering Functions
  • 4.2 Li Auto
  • Lixiang Tongxue: Building Multiple Scenarios
  • Lixiang Tongxue: Agent Architecture - Two Paths
  • Lixiang Tongxue: Ordering Scenario Analysis
  • Lixiang Tongxue: Payment Scenario Analysis
  • Lixiang Tongxue: Key Points of AI Master R&D
  • Lixiang Tongxue: CoT interpretability
  • Mind GPT (1)-(2)
  • Foundation Model Training Platform
  • Lixiang Tongxue: Foundation Model Capability Upgrade (1)-(2)
  • Lixiang Tongxue: Multimodal Interaction Cases
  • 4.3 Xpeng
  • History of AI Foundation Model Applications
  • Intelligent Cockpit Solutions: XOS
  • Application Cases: (1)-(4)
  • 4.4 Xiaomi
  • Automotive Foundation Models: MiLM
  • Voice Capabilities of XiaoAi Tongxue
  • Voice Task Parsing and Execution Process
  • Vehicle Recognition Functions (1)-(3)
  • Scenario Construction of XiaoAi Tongxue
  • Cases: Cockpit of 2026 New SU7 (1)-(4)
  • 4.5 Leapmotor
  • Foundation Model 1.0
  • Foundation Model 2.0
  • 4.6 BYD
  • Cockpit AI: From Independent R&D to Collaboration
  • Dynamics in Agent Collaboration
  • Application Cases of Xuanji AI Foundation Models in Cockpits
  • 4.7 Geely
  • Xingrui AI Foundation Model
  • Architecture of Xingrui AI Foundation Model
  • Full-Domain AI System 1.0
  • Full-Domain AI System 2.0: Release
  • Full-Domain AI System 2.0: Architecture
  • Full-Domain AI System 2.0: Cockpit-Driving Integration Agent
  • Application Forms of Foundation Models in Cockpits
  • Flyme Auto Voice Interaction Capability
  • ZEEKR: Application Cases of Super EVA
  • ZEEKR: Agent Scenarios (1)-(2)
  • 4.8 Chery
  • LION AI Foundation + iFlytek Spark Model + DeepSeek
  • Super Agent: Xiaoqi
  • AI Applications in Audio System
  • AI Cockpit Configuration in Vehicle Models
  • 4.9 Changan
  • AI Model Matrix
  • AI Agent Matrix
  • SDA Intelligence System
  • Applications of AI Technology: (1)-(2)
  • Automatic Switching of Cockpit Scenarios and Functions
  • 4.10 Great Wall Motor
  • Coffee Agent System: Application Scenarios
  • AI System Foundation
  • Improving the "Q&A Mode"
  • Agent Service System Based on Foundation Models
  • Upgrading the Overall Agent with a New Platform
  • WEY Equipped with Cockpit Agent
  • 4.11 SAIC
  • Applications of IM Foundation Models in Automotive Voice
  • IM Foundation Model Application Case: IM L6
  • IM AI Foundation Models Build Active Perception Scenarios
  • DeepSeek Applied by SAIC
  • SAIC Super Agent Adopts a Full-Domain Fusion Intelligent Architecture
  • Application Scenarios of SAIC Super Agent
  • IM Agent Application Case: LS9
  • 4.12 GAC
  • Intelligent Cockpit Solution: ADiGO SPACE
  • Applications of AI Foundation Models in Cockpits
  • Applications of DeepSeek in Cockpits
  • Edge-Cloud Integration Architecture of ADiGO Intelligence
  • 4.13 BAIC
  • Development History of Cockpit AI System
  • Cockpit AI 2.0: Product Architecture of Baimo Huichuang
  • Applications of DeepSeek: Access to Baimo Huichuang
  • Cockpit AI Scenarios (1)-(2)
  • Cockpit AI Cross-Domain Fusion Era: Yuanjing AI
  • Three Levels of Baimo Huichuang AI Promoting Full-Domain Integration
  • 4.14 FAW
  • Applications of DeepSeek (1)-(2)
  • Lingxi Cockpit: Implementing AI Scenario Services Through Full-Stack Independent R&D
  • Lingxi Cockpit: Application Scenarios of AI Functions
  • Lingxi Cockpit: Applications of AI Functions
  • 4.15 Dongfeng Motor
  • Development of Cockpit AI System
  • Applications of DeepSeek: (1)-(2)
  • Cockpit AI System Based on "Tianyuan Architecture": From OS to Models, and Then to Cockpit Functions
  • Cockpit AI System Based on "Tianyuan Architecture": Foundation Model Application Architecture
  • Cockpit AI System Based on "Tianyuan Architecture": Cockpit Scenario Highlights
  • Cockpit AI System Based on "HarmonySpace": "Xiaoyao Cockpit 2.0"
  • AI Application: AI Agent of Voyah Courage
  • AI Application: Cockpit AI Technology Research Direction
  • Collaboration with Yan AI
  • AI Application Cases of Nissan
  • 4.16 JAC
  • AI Cockpits: (1) - (2)
  • 4.17 Tesla
  • Grok Cockpit Model Got Upgraded
  • 4.18 BMW
  • Overseas and Chinese Cockpit AI Solutions
  • BMW Intelligent Voice Assistant 2.0 Based on LLM
  • 4.19 Mercedes-Benz
  • MB.OS Digital World Delivers Personalized Services with MBUX Virtual Assistant
  • 4.20 Stellantis
  • Automotive AI Applications (1)
  • Automotive AI Applications (2)
  • 4.21 Others
  • Volkswagen Voice Interaction System Equipped with GPT
  • Toyota Cockpit AI Foundation Models Deployed