콕핏-운전 통합 : 센트럴 도메인 컨트롤러 SoC 및 AI 슈퍼컴퓨팅 아키텍처 시장(2026년)

Cockpit-Driving integration and AI supercomputing research: The One Chip solution is rapidly installed in vehicles, and AI supercomputing architectures are moving towards full-domain integration.

AI supercomputing architecture layout: OEMs focus on full-domain integration, while Tier 1 suppliers enter the field with software and hardware solutions.

Currently, leading Chinese OEMs have built an EEA system of "1 central computing platform + 2-3 ZCUs". Most OEMs have equipped their vehicles with a unified "central brain" and efficient "neural network", and are gradually integrating AI applications on this basis.

Great Wall Motor's GWM*ONE platform, as the first native AI full-power platform, uses SOA as its core in its underlying system. It innovatively granulates a vehicle into more than 300 multiplexable functional units, and achieves free cross-vehicle model invocation and seamless cross-scenario collaboration through a full-stack standardized service contract system.

Based on Coffee EEA 4.0, the platform integrates the AI OS and dual VLA models to achieve unified management of vehicle perception, decision and execution. Moreover, its world's first bionic motion control system opens up the boundaries between chassis, intelligent driving and powertrain system, realizing vehicle coordinated control, and supports +-10° rear wheel steering and crab mode, and enables stable braking in straight line single-sided dual-wheel tire burst at 160+ km/h and other control function clusters.

SAIC IM's super agent, IM Ultra Agent, is composed of three major technical pillars at the bottom - the IM Fusion Nova cockpit-driving integration full-domain fusion architecture, the IM AD ZETA intelligent driving foundation model developed with Momenta, and Alibaba Qwen Model that is available in production vehicles. The fully wire-controlled Lingxi Digital Chassis was also released as an execution carrier. From the underlying heterogeneous computing architecture level, this architecture completely connects the three core systems of chassis-by-wire, intelligent driving AI, and intelligent cabin AI, and builds a three-layer architecture of "global cerebrum + agile cerebellum + execution body":

Global cerebrum: Qwen Model is responsible for understanding user intentions, global scenario scheduling, and multi-task coordination;

Agile cerebellum: IM AD ZETA is responsible for driving scenario decision, risk prediction, and vehicle motion control;

Execution body: A fully digital chassis-by-wire is responsible for accurately and quickly converting AI decisions into vehicle physical actions.

Volkswagen Group is deploying full-domain agent AI locally through cooperation with companies such as XPeng and Horizon Robotics:

CEA 1.0/1.3 (2025-2026): The core of the current CEA 1.0 and its iterative version CEA 1.3 is to complete the transformation from a centralized architecture to a quasi-central + zonal architecture, and realize implementation of core technologies such as 800V high voltage, 8295 cockpit chip, high-level intelligent driving, and full-domain OTA, covering multiple brands such as Volkswagen, Audi, and Skoda with varying sizes (from Class A to D) and powertrain forms (battery-electric, extended-range, etc.). A number of new cars will be launched intensively in 2026.

CEA 2.0/3.0 (2027-2030): CEA 2.0 will deeply integrate the central computing and central gateway to further improve the level of integration, pre-install L4 intelligent driving hardware, and enable vehicle-cloud integrated interaction. CEA 3.0 will be embedded in native AI foundation models to achieve complete decoupling of software and hardware, and support on-demand subscription of functions, and continuous iteration. Volkswagen also self-develops chips. Its chip company, CARIZON, is developing a chip code-named C7H whose computing power can be flexibly expanded between 600~2400TOPS. It is optimized for multi-modal foundation models and is expected to find large-scale automotive application in 2028.

Software and hardware suppliers have proposed forward-looking solutions around the AI supercomputing architectures in terms of central computing platforms, AIOS, AI agents, and foundation models. The Aqua Drive OS 2.0 Pre released by ThunderSoft in 2026 is directly positioned as an "AI native vehicle operating system", and emphasizes its in-depth collaboration with AIBOX, which can not only promote the large-scale automotive application of foundation models, but also support the implementation of AI agents in scenarios.

The newly upgraded Aqua Drive OS 2.1 is based on the NVIDIA Nemo Claw reference software stack and has been globally adapted on Qualcomm Snapdragon 8397/8797. Relying on the standardized L+A architecture, it fully implements full-link capabilities including AI reasoning, BSP, middleware and agent management. The "AIOS+AIBOX" integrated solution was also proposed. The AIBOX is equipped with NVIDIA DRIVE AGX, which can provide up to 200TOPS of AI computing power and 205GB/s transmission bandwidth, and is the first to smoothly run 7B foundation models on the device.

2026 is the first year of physical AI. Vehicles obtain information from the real world via "senses" such as cameras, LiDAR, and tactile sensors, combine it with AI foundation models to understand physical laws and make decisions, and then output a range of physical actions through "limbs" such as motors and joints. Finally, they continue to correct errors and evolve themselves from real feedback. Autonomous driving is an important scenario for implementation of physical AI in the field of intelligent driving.

QCraft's physical AI model is based on the "world model + reinforcement learning" unified architecture. It is composed of cloud world model + vehicle world behavior model, which can achieve three major upgrades: stronger reasoning, better decision, and wider generalization.

The cloud world model plays the role of "creator": using natural language instructions to generate extreme driving scenarios, batch production of long-tail cases that are difficult to encounter in real road tests, and used for continuous training of foundation models.

The automotive world model is the "executor", which is an encapsulated VLA and online world model. With the world model + reinforcement learning unified architecture, multi-modal perception and real-time trajectory generation are deeply coupled to complete a real-time closed loop from prediction to decision.

The core of QCraft's world model lies in the joint cloud-vehicle training, which can not only cover 90% of regular scenarios, but also reduce blind spots in the remaining 10% of edge scenarios. Under this architecture, the "Driven-by-QCraft MAX" intelligent driving solution, based on the 500TOPS automotive computing power platform, can benchmark against the urban NOA experience with thousands of TOPS.

AI supercomputing platform: The One Chip solution will gradually become mainstream, and domestic chips will be deployed more rapidly.

Vehicle EEAs are evolving from distributed to domain centralized and then to centralized forms. Cockpit-Driving integration is the core link of cross-domain fusion, and One Chip is the final form of cockpit-driving integration. The industry has passed through two stages: One Box/Two Board (two boards are integrated on the same domain controller) and One Box/One Board (two chips are integrated on the same PCB). In 2025, the mass production of One Chip solutions just began. Native cockpit-driving integration chips represented by NVIDIA Thor (2000TOPS computing power), Qualcomm SA8797P (320TOPS dense computing power), and Horizon Robotics "Starry" have been launched one after another, making it possible for a single chip to simultaneously support L3/L4 intelligent driving, multi-screen cockpit interaction, APA and other functions, acting as the core carrier of the future central computing platform.

In 2026, single-chip solutions such as Qualcomm SA8775P/SA8797 and Black Sesame Wudang C1296 will enter the mass production cycle. For example, AutoLink World's cockpit-driving integration controller based on Snapdragon 8797 is expected to be mass-produced in 2026; as the self-developed chip solutions (Turing chip, Shenji NX9031, Mach 100) of emerging OEMs such as XPeng, NIO, and Li Auto are quickly available in vehicles and Horizon Starry enters all ecosystems, One Chip solutions will take an increasing share in cockpit-driving integration.

In October 2025, new Arcfox aT5, the world's first production vehicle model with cockpit-driving integration based on Qualcomm SA8775P, was officially launched. It is equipped with AutoLink World's domain controller solution, which is connected to a front radar, 12 ultrasonic radars, and 7 cameras (front view stereo camera, surround view, rear view), realizing L2+ Highway NOA, and integrated parking and HPA.

The Leapmotor D19, launched in April 2026, is equipped with dual Qualcomm Snapdragon 8797 chips. With computing power up to 1280 TOPS, they can realize foundation model-based cockpits and VLA-based intelligent driving functions.

Honda Motor has officially announced cooperation with Renesas R-Car X5. The next-generation vehicle models of the Honda 0 Series will be upgraded to a quasi-central + zonal architecture. The central ECU is planned to be equipped with an SoC using TSMC's 3nm process and multi-die chiplet technology. Renesas' fifth-generation R-Car X5 SoC will be combined with Honda's self-developed AI accelerator, with the target AI computing power of up to 2000TOPS.

In China, there are One Chip solutions like Black Sesame Wudang C1296, Horizon Robotics "Starry" and FAW Hongqi No.1.

Black Sesame Wudang C1296: Based on the 7nm advanced process, the CPU computing power is about 208K DMIPS, the GPU computing power is about 1.5TFLOPS, and the NPU computing power is 76TOPS. With the "hardware isolation + Hypervisor" architecture, the resource integration and safe isolation of functional domains such as intelligent cockpit, intelligent driving, and body control are realized at the hardware level for the first time. Simultaneously, it possesses abundant computing power and interface capabilities, with a single chip covering multiple core scenarios ranging from intelligent cockpit and driving-parking integration to vehicle computing. Currently, it has been officially designated for Dongfeng's "Tianyuan Intelligent Cockpit Plus", and will be first installed in Dongfeng eπ 007, fully supporting foundation models and voice interaction, L2+ intelligent driving, and FAPA.

FAW Hongqi No.1: Defined as a multi-domain fusion chip, it integrates five functional domains: driving assistance, intelligent cockpit, vehicle body control, communication, and safety; compared to mainstream domain fusion chips in the industry (such as Qualcomm SA8775), its logic computing capability is improved by 21.7%, and its image processing capability is improved by 15.4%; it has a built-in independent security island, hardware-level isolation, supports ASIL-D functional safety, and meets the Level 2 information security requirements of "home-made cryptographic algorithms recognized by the National Cryptographic Administration of China". Currently, it has been successfully developed and is ready to enter the vehicle testing phase.

Horizon Robotics "Starry": As the first cockpit-driving integrated vehicle agent chip in China, Starry 6P uses a 5nm process, 650TOPS computing power and 273GB/s bandwidth to support local operation of on-device foundation models. It also supports information display of 6-12 screens to meet the rendering requirements of automotive entertainment and multimedia display. It adopts the Fortress-safe physical isolation architecture to achieve physical isolation and independent operation of the cockpit and intelligent driving. Combined with the vehicle agent operating system KakaClaw and the HSD intelligent driving system, it lays the foundation for AI supercomputing architecture in advance. The cockpit-driving integration vehicle intelligent solution based on Starry 6 will be first launched globally with Chery iCar V27.

AI supercomputing communication architecture: automotive fiber optic communication technology will be introduced in the next stage.

The AI supercomputing architecture also places stringent requirements on automotive equipment interconnection, requiring high bandwidth, stable and reliable, redundant protection, low latency, and deterministic on-board backbone networks. At present, the backbone network has been upgraded from CAN bus to Gigabit/10G Ethernet to meet the demand for massive data transmission. In the future, with the evolution of AI supercomputing architecture, automotive optical communication will become an effective way and key solution to support communications with high bandwidth requirements in automobiles. The trend of optical advancement and copper withdrawal in the automotive industry has emerged, and automotive optical communication technology is entering industrialization.

Automotive fiber optic communication refers to the communication technology that uses "light waves" as the information carrier and optical fiber as the transmission medium, transmits data in the optical fiber through optical signals, and realizes information transmission by electrical/optical and optical/electrical mutual conversion, thereby realizing high-speed, real-time, and anti-interference data interconnection between various in-vehicle electronic control units (ECUs, sensors, display devices, computing units, etc.).

Currently, there are two main routes for autonomous optical communication inside and outside China:

1. Automotive fiber optic Ethernet: Based on the development of fiber optic Ethernet technology, it adopts a point-to-point communication mode. Its core lies in switching. It is suitable for application scenarios dominated by east-west traffic and follows the IEEE802.3cz protocol;

2. Automotive PON: PON is developed based on traditional optical PON technology and adopts a point-to-multipoint communication mode. Its core feature is passivity. Built on passive optical splitters, it eliminates the need for switches and active components, making it well-suited for in-vehicle application scenarios dominated by north-south traffic.

The similarity between the two is that they are new technologies based on existing technologies - PON and Ethernet, and designed and optimized to adapt to the automotive environment. However, there are major differences in functions and performance between automotive Ethernet and automotive PON. These differences make it difficult for Ethernet to serve as an independent technology to cater to all requirements of automotive communication networks.

Currently, Chinese OEMs have proposed many products related to "automotive fiber optic Ethernet" and are actively collaborating with Tier 1 suppliers to develop system solutions for real-vehicle verification by OEMs. For example, Li Auto and Hinge Technology have jointly developed an automotive optical communication test bench, which has passed A-sample delivery and B-sample testing, with a goal of mass production and automotive deployment in 2026.

In January 2026, AutoLink World and ReinOCS Technologies, a subsidiary of Zhongji Innolight, jointly released Deep Fusion EEA, which consists of three parts: a central computing platform based on Qualcomm 8797, a domain controller based on AMD Versal AI Edge Gen 2, and a high-speed optical communication backbone network based on optical PCIe communication technology. The central platform provides high computing power AI and flexible scheduling, and the domain controller ensures function convergence and rapid execution. High-speed optical communication breaks through the bottleneck of traditional wiring harnesses, and the transmission supports higher density sensing and data flows.

In Deep Fusion EEA, the high-speed optical communication solution is provided by ReinOCS Technologies, demonstrating competitive underlying technical capabilities:

All-Scenario Adaptability: It supports lossless transmission of 8K@60Hz ultra-high-definition video, meeting the data interaction needs of multi-screen interaction in intelligent cockpits and autonomous driving high-resolution sensors (such as LiDAR, high-definition cameras), with a maximum transmission distance of up to 100 meters, covering vehicle multi-region device connections;

Extreme Environmental Adaptability: Utilizing lightweight design and anti-electromagnetic interference technology, it can operate stably in extreme automotive environments ranging from -40°C to 85°C, solving the signal attenuation problem of traditional copper cable transmission in complex electromagnetic environments;

Special Optical and Structural Design: Both modules and wiring harnesses can withstand high-acceleration shocks and continuous vibrations across the entire frequency band in automotive scenarios, fully meeting the reliability standards for automotive wiring harnesses and connections. Even in complex operating environments such as frequent vehicle starts and stops and bumpy road conditions, it can ensure the stability and continuity of data transmission;

Architectural Scalability: Adopting a modular interface design, it can seamlessly adapt to the multi-sensor fusion requirements of future high-level autonomous driving (L4 and above), providing OEMs with a flexible solution of " upgradeable hardware and iterable software," reducing the long-term upgrade costs of vehicle electronic architecture.

Table of Contents

Industry Summary 1: Overall Trend of OEM EEA

Industry Summary 2: Overall Trend of AI Supercomputing Architectures in China's Passenger Car Market

Industry Summary 3: Overall Trend of AI Agents in China's Passenger Car Market

1 Forecast for Cockpit-Driving Integration Market Size and Penetration Rate

• 1.1 Forecast for Penetration Rate of Central Computing Architecture in Local Passenger Cars in China
• Penetration Rate of Central Computing Architecture in Local Passenger Cars in China, 2023-2030E
• Attached Table: Penetration Rate of Central Computing Architecture in Local Passenger Cars in China, 2023-2030E
• 1.2 Forecast for Penetration Rate of Cockpit-Driving Integration Solutions in Passenger Cars in China
• Classification Logic of Cockpit-Driving Integration Solutions for Passenger Cars in China
• Penetration Rate of Cockpit-Driving Integration Solutions in Passenger Cars in China, 2023-2030E
• Attached Table: Penetration Rate of Cockpit-Driving Integration Solutions in Passenger Cars in China, 2023-2030E
• 1.3 Forecast for Penetration Rate of Central Computing Platforms in Passenger Cars in China
• Evolution of Cockpit-Driving Integration Solution Integration Forms: One Box, One Board, One Chip
• Penetration Rate of Central Computing Platforms (One Board One Chip), 2025-2030E
• Penetration Rate of Central Computing Platforms (One Board Multi Chips), 2025-2030
• Penetration Rate of Central Computing Platforms (One Box Multi Boards), 2025-2030
• Attached Table: Penetration Rate of Cockpit-Driving Integrated Solutions in Local Passenger Cars in China by Integration Form, 2025-2030E

2 Summary of Cockpit-Driving Integration and AI Supercomputing Architecture Trends

• 2.1 Cockpit-Driving Integration Architecture Design: Central Computing + ZCU Architecture
• Iterative Process of Automotive EEA Forms
• Automotive EEA Forms: Central Computing + ZCU Architecture (1)
• Automotive EEA Forms: Central Computing + ZCU Architecture (2)
• Deployment Forms of Vehicle Central + Zonal Architecture (1)
• Deployment Forms of Vehicle Central + Zonal Architecture (2)
• Deployment Forms of Vehicle Central + Zonal Architecture (3)
• Deployment Forms of Vehicle Central + Zonal Architecture (4)
• Development Directions of Vehicle Central + Zonal Architecture (1)
• Development Directions of Vehicle Central + Zonal Architecture (2)
• Development Directions of Vehicle Central + Zonal Architecture (3)
• Development Directions of Vehicle Central + Zonal Architecture (4)
• 2.2 OEMs: Summary of Cockpit-Driving Integration and AI Supercomputing Integration Solutions
  • 2.2.1 AI Supercomputing Architectures of OEMs
  • Summary of AI Supercomputing Architectures of OEMs (1)
  • Summary of AI Supercomputing Architectures of OEMs (2)
  • Summary of AI Supercomputing Architectures of OEMs (3)
  • Summary of AI Supercomputing Architectures of OEMs (4)
  • 2.2.2 Cockpit AI Solutions of OEMs
  • Summary of Cockpit AI Solutions of OEMs (1)
  • Summary of Cockpit AI Solutions of OEMs (2)
  • Summary of Cockpit AI Solutions of OEMs (3)
  • Summary of Cockpit AI Solutions of OEMs (4)
  • 2.2.3 Intelligent Driving AI Solutions of OEMs
  • Summary of Intelligent Driving AI Solutions of OEMs (1)
  • Summary of Intelligent Driving AI Solutions of OEMs (2)
  • Summary of Intelligent Driving AI Solutions of OEMs (3)
  • Summary of Intelligent Driving AI Solutions of OEMs (4)
  • 2.2.4 Chassis AI Solutions of OEMs
  • Summary of Chassis AI Solutions of OEMs (1)
  • Summary of Chassis AI Solutions of OEMs (2)
  • Summary of Chassis AI Solutions of OEMs (3)
  • Summary of Chassis AI Solutions of OEMs (4)
  • Summary of Powertrain AI Solution of OEMs (1)
  • Summary of Powertrain AI Solution of OEMs (2)
  • Summary of Powertrain AI Solution of OEMs (3)
• 2.3 Tier 1 Suppliers: Summary of AI Supercomputing Fusion and AI Box Solutions
  • 2.3.1 AI Supercomputing Fusion Solutions of Tier1 Suppliers
  • Summary of Central Computing Platform Solutions of Tier1 Suppliers (1)
  • Summary of Central Computing Platform Solutions of Tier1 Suppliers (2)
  • Summary of Central Computing Platform Solutions of Tier1 Suppliers (3)
  • Summary of Vehicle Operating System Solutions of Tier1 Suppliers (1)
  • Summary of Vehicle Operating System Solutions of Tier1 Suppliers (2)
  • 2.3.2 AI Box Solutions of Tier1 Suppliers
  • Cockpit AI Box (1)
  • Cockpit AI Box (2)
  • Cockpit AI Box (3)
  • Cockpit AI Box (4)
  • Summary of OEM Cockpit AI Box Products (1)
  • Summary of OEM Cockpit AI Box Products (2)
  • Cockpit AI Box Product Solution 1 (1-3)
  • Cockpit AI Box Product Solution 2
  • Cockpit AI Box Product Solution 3
  • Cockpit AI Box Product Solution 4
  • Cockpit AI Box Product Solution 5
  • Cockpit AI Box Product Solution 6
• 2.4 Summary of Solutions of Chip Vendors
• Cockpit-Driving Integration Central Domain Controller Soc Classification Logic
• Central Domain Controller SoC Selection for Cockpit-Driving Integration Solutions (1)
• Central Domain Controller SoC Selection for Cockpit-Driving Integration Solutions (2)
• One Board One Chip Solutions (1)
• One Board One Chip Solutions (2)
• One Board One Chip Solutions (3)
• Central Domain Controller Platform with One Board One Chip
• 2.4.1 Qualcomm SA8797
• Qualcomm SA8797P-based Cockpit-Driving Integration Domain Controller Solutions and Main Customers
• Qualcomm SA8797P: Performance Features (1)
• Qualcomm SA8797P: Performance Features (2)
• Qualcomm SA8797P: Performance Features (3)
• Qualcomm SA8797P-based Cockpit-Driving Integration Domain Controller Solution 1
• Qualcomm SA8797P-based Cockpit-Driving Integration Domain Controller Solution 2
• Qualcomm SA8797P-based Cockpit-Driving Integration Domain Controller Solution 3
• Qualcomm SA8797P-based Cockpit-Driving Integration Domain Controller Solution 4
• 2.4.2 Qualcomm 8775
• Qualcomm SA8775P-based Cockpit-Driving Integration Domain Controller Solutions and Main Customers
• Qualcomm SA8775P: Performance Features (1-4)
• Qualcomm SA8775P-based Cockpit-Driving Integration Domain Controller Solutions
• 2.4.3 Black Sesame Wudang C1296
• Black Sesame Wudang C1296-based Cockpit-Driving Integration Domain Controller Solutions and Main Customers
• Black Sesame Wudang C1296 (1-3)
• Black Sesame Wudang C1296-based Cockpit-Driving Integration Domain Controller Solution (1-3)
• 2.4.4 Horizon Robotics "Starry"
• Horizon Robotics "Starry" (1)
• Horizon Robotics "Starry" (2)
• Horizon Robotics "Starry" (5)
• Vehicle Agent Operating System (1)
• Vehicle Agent Operating System (2)
• Intelligent Driving Solution: HSD v1.6
• 2.4.5 Renesas R-Car X5H
• Gen5 Multi-Domain Fusion SoC (1)
• Gen5 Multi-Domain Fusion SoC (2)
• Gen5 Multi-Domain Fusion SoC (6)
• Gen5 R-CAR (1)
• Gen5 R-CAR (2)
• 2.4.6 "Hongqi No.1" of FAW Group
• Hongqi No.1
• 2.4.7 NVIDIA Thor
• NVIDIA DRIVE Thor-based Cockpit-Driving Integration Domain Controller Solutions and Main Customers
• NVIDIA DRIVE Thor (1-4)
• NVIDIA DRIVE Thor-based Cockpit-Driving Integration Domain Controller Solution (1-3)
• 2.4.8 Qualcomm 8397
• Qualcomm AI Intelligent Cockpit Chip (1)
• Qualcomm AI Intelligent Cockpit Chip (2)
• Qualcomm AI Intelligent Cockpit Chip Solution
• 2.4.9 Mach 100 of Li Auto
• Mach 100 (1-4)
• 2.4.10 NIO Shenji NX9031
• Shenji NX9031 (1-4)
• 2.4.11 "Turing" AI Chip of XPeng
• "Turing" Chip (1-4)
• 2.4.12 SiEngine Longying No.2
• SiEngine's Cockpit-Driving Integration Chip
• SiEngine's AI Accelerator Chip
• 2.4.13 Black Sesame Huashan A2000
• Black Sesame Huashan (1)
• Black Sesame Huashan (2)
• Black Sesame Huashan (5)
• Black Sesame Huashan Solution
• 2.4.14 AXERA M97
• AXERA's Cockpit-Driving Integrated Sensing Chip (1)
• AXERA's Cockpit-Driving Integrated Sensing Chip (2)
• AXERA's Cockpit-Driving Integrated Sensing Chip (3)
• 2.4.15 SemiDrive X10
• SemiDrive's AI Intelligent Cockpit Chip: X10 (1-4)
• 2.4.16 MediaTek C-X1
• MediaTek's AI Intelligent Cockpit Chip (1-4)
• 2.5 Integration Trend of Cockpit-Driving Integration and AI Supercomputing
  • 2.5.1 Trend 1: MPAM Technology Improves the Anti-Interference Capability of Cockpit-Driving Integration SoCs
  • MPAM Technology (1)
  • MPAM Technology (2)
  • MPAM Technology (3)
  • MPAM Technology (4)
  • 2.5.2 Trend 2: Automotive Fiber Optic Communication Is Adapted to AI Supercomputing Architectures
  • AIDVs (AI-Defined Vehicles) (1)
  • AIDVs (AI-Defined Vehicles) (2)
  • Automotive Fiber Optic Communication Automotive Fiber Optic Communication Architecture Design
  • Comparison between Automotive Fiber Optic Ethernet vs. Automotive Optical PON (1-3)
  • Requirements for Applying Optical Communication Technology to Vehicles
  • Automotive Fiber Optic Communication: Key Technologies and Technical Challenges (1)
  • Automotive Fiber Optic Communication: Key Technologies and Technical Challenges (2)
  • Summary of OEM Automotive Fiber Optic Communication Layout Strategies
  • Tier 1 Automotive Fiber Optic Communication Architecture Solution 1-3
  • 2.5.3 Trend 3: Next-Generation Automotive Storage Technology Is Being Laid out
  • Automotive Storage Demand (1)
  • Automotive Storage Demand (2)
  • Development Process of Automotive Storage Technology
  • Automotive Storage Technology: LPDDR5X
  • Automotive Storage Technology: Next-Generation LPDDR6
  • Automotive Storage Technology: Next-Generation UFS 4.1
  • 2.5.4 Trend 4: AI Agents Enter the Automotive Cockpit Field
  • AI Agents (Agentic AI) (1-4)
  • AI Agents (Agentic AI) Enter the Automotive Cockpit Field
  • Cross-Domain Integration AI Agent Layout of OEMs (1)
  • Cross-Domain Integration AI Agent Layout of OEMs (2)
  • Cross-Domain Integration AI Agent Layout of Tier 1 Suppliers (1-4)
  • 2.5.5 Trend 5: Autonomous Driving Is the "Best Entry Point" for Physical AI
  • Physical AI (1)
  • Physical AI (2)
  • Physical AI (5)
  • Summary of Physical AI Layout Strategies of OEMs (1)
  • Summary of Physical AI Layout Strategies of OEMs (2)
  • Summary of Physical AI Layout Strategies of Tier 1 Suppliers

3 AI Supercomputing Architecture Layout Strategies of Domestic OEMs

• 3.1 Great Wall Motor
• AI Supercomputing Architecture (1-2)
• Physical Architecture (1-3)
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• AI Supercomputing Architecture Layout Strategy (3)
• Foundation Models
• Chassis AI
• Vehicle Models with AI Supercomputing Architecture
• 3.2 GAC Group
• AI Supercomputing Architecture Layout (1)
• AI Supercomputing Architecture Layout (2)
• Physical Architecture (1-5)
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• AI Supercomputing Architecture Layout Strategy (3)
• Cockpit AI (1)
• Cockpit AI (2)
• Cockpit AI (5)
• Intelligent Driving AI
• Powertrain AI (1-3)
• Chassis AI
• 3.3 SAIC Motor
• AI Supercomputing Architecture Layout (1-3)
• AI Supercomputing Architecture (1-2)
• AI Supercomputing Architecture Layout Strategy (1-3)
• Intelligent Driving AI (1-2)
• Cockpit AI (1-2)
• Chassis AI (1-2)
• Roewe's AI Supercomputing Architecture (1-3)
• 3.4 Chery
• AI Supercomputing Architecture (1)
• AI Supercomputing Architecture (2)
• Physical Architecture (1)
• Physical Architecture (2)
• Physical Architecture (3)
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• AI Supercomputing Architecture Layout Strategy (5)
• Intelligent Driving AI
• Cockpit AI
• Chassis AI
• Powertrain AI
• 3.5 Changan Automobile
• AI Supercomputing Architecture Layout
• AI Supercomputing Architecture (1)
• AI Supercomputing Architecture (2)
• Physical Architecture: SDA 2.0 (1-4)
• AI Supercomputing Architecture Layout Strategy (1-4)
• Intelligent Driving AI
• Cockpit AI
• Chassis AI (1-3)
• 3.6 Geely
• AI Supercomputing Architecture Layout (1-2)
• Physical Architecture (1-2)
• ZEEKR's Physical Architecture (1)
• ZEEKR's Physical Architecture (2)
• AI Supercomputing Architecture Layout Strategy (1-4)
• Cockpit AI (1-3)
• Intelligent Driving AI (1-3)
• Power AI
• Chassis AI (1-4)
• Robotaxi's AI Supercomputing Architecture Layout
• Robotaxi's Physical Architecture (1)
• Robotaxi's Physical Architecture (2)
• Robotaxi's Intelligent Driving AI
• 3.7 BAIC
• AI Supercomputing Architecture Layout (1-5)
• Physical Architecture (1-2)
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• AI Supercomputing Architecture (1-3)
• 3.8 FAW
• AI Supercomputing Architecture (1-4)
• Physical Architecture
• AI Supercomputing Architecture Layout Strategy (1-3)
• Foundation Models
• Intelligent Driving AI (1-2)
• Cockpit AI (1-3)
• Chassis AI (1-2)
• 3.9 BYD
• AI Supercomputing Architecture (1-3)
• AI Supercomputing Physical Architecture (1-3)
• AI Supercomputing Architecture Layout Strategy (1-3)
• Foundation Models (1-2)
• Intelligent Driving AI (1-2)
• Cockpit AI
• Chassis AI (1-2)
• Power AI
• 3.10 Dongfeng Motor
• AI Supercomputing Architecture Layout (1)
• AI Supercomputing Architecture Layout (2)
• Physical Architecture (1)
• Physical Architecture (2)
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• Voyah's AI Supercomputing Architecture Layout Strategy
• Foundation Models
• AIOS
• Cockpit AI (1-3)
• Voyah's AI Supercomputing Architecture Layout (1)
• Voyah's AI Supercomputing Architecture Layout (2)
• Voyah's AI Supercomputing Architecture Layout (5)
• 3.11 Li Auto
• Physical Architecture (1-3)
• AI Supercomputing Architecture Layout Strategy (1-3)
• AIOS (1-4)
• Intelligent Driving AI (1)
• Intelligent Driving AI (2)
• Intelligent Driving AI (3)
• Intelligent Driving AI (1)
• Intelligent Driving AI (2)
• Intelligent Driving AI (3)
• Cockpit AI (1)
• Cockpit AI (2)
• Cockpit AI (3)
• Chassis AI
• 3.12 NIO
• AI Supercomputing Architecture
• Physical Architecture (1)
• Physical Architecture (2)
• Physical Architecture (5)
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• AIOS (1-6)
• Intelligent Driving AI (1-3)
• Cockpit AI (1-3)
• Chassis AI (1-5)
• 3.13 XPeng
• AI Supercomputing Architecture Layout (1)
• AI Supercomputing Architecture Layout (2)
• AI Supercomputing Architecture Layout Strategy (1-3)
• Robotaxi AI Supercomputing Architecture Layout Strategy
• AIOS (1)
• AIOS (2)
• Cockpit AI (1-3)
• Intelligent Driving AI (1-4)
• Robotaxi Chassis AI (1-4)
• Robotaxi Intelligent Driving AI (1-2)
• 3.14 Leapmotor
• Physical Architecture
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• Intelligent Driving AI
• Intelligent Driving AI
• Chassis AI (1-4)
• 3.15 Xiaomi Auto
• Physical Architecture (1)
• Physical Architecture (2)
• AI Supercomputing Architecture Layout Strategy (1-3)
• Cockpit AI
• Intelligent Driving AI (1)
• Intelligent Driving AI (2)
• Chassis AI (1-2)
• 3.16 Harmony Intelligent Mobility Alliance (HIMA)
• Physical Architecture
• AI Supercomputing Architecture Layout Strategy (1-5)
• Intelligent Driving AI (1-5)
• Cockpit AI (1-3)
• Chassis AI (1-4)
• Powertrain AI (1-4)

4 AI Supercomputing Architecture Layout Strategies of Overseas OEMs

• 4.1 Tesla
• Cybertruck's Physical Architecture (1)
• Cybertruck's Physical Architecture (2)
• Physical Architecture (1)
• Physical Architecture (2)
• AI Supercomputing Architecture Layout Strategy (1-3)
• Intelligent Driving AI (1-3)
• Cockpit AI (1-3)
• Cybertruck's Chassis AI (1)
• Cybertruck's Chassis AI (2)
• 4.2 Volkswagen
• AI Supercomputing Architecture Layout (1)
• AI Supercomputing Architecture Layout (2)
• AI Supercomputing Architecture (1)
• AI Supercomputing Architecture (2)
• AI Supercomputing Architecture (3)
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• AI Supercomputing Architecture Layout Strategy (3)
• AI Supercomputing Architecture Layout Strategy (4)
• Intelligent Driving AI
• Chassis AI
• 4.3 BMW
• Physical Architecture (1)
• Physical Architecture (2)
• Physical Architecture (6)
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• Cockpit AI (1)
• Cockpit AI (2)
• Cockpit AI (5)
• Power AI
• 4.4 Daimler
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• AIOS (1)
• AIOS (2)
• Cockpit AI
• Intelligent Driving AI (1)
• Intelligent Driving AI (2)
• Chassis AI (1)
• Chassis AI (2)
• 4.5 Toyota
• AI Supercomputing Architecture Layout
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• AI Supercomputing Architecture Layout Strategy (3)
• Intelligent Driving AI (1-4)
• Cockpit AI (1-3)
• Chassis AI
• 4.6 Honda
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• AI Supercomputing Architecture Layout Strategy (2)
• AIOS
• Intelligent Driving AI (1)
• Intelligent Driving AI (2)
• Cockpit AI (1-3)
• 4.7 SAIC-GM
• AI Supercomputing Architecture (1)
• AI Supercomputing Architecture (2)
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• Cockpit AI (1)
• Cockpit AI (2)
• Cockpit AI (5)
• Intelligent Driving AI
• Chassis AI (1)
• Chassis AI (2)
• Power AI
• 4.8 Nissan
• AI Supercomputing Architecture Layout Strategy (1)
• AI Supercomputing Architecture Layout Strategy (2)
• AI Supercomputing Architecture Layout Strategy (3)
• Intelligent Driving AI

5 Software and Hardware Solutions of Tier1 Suppliers

• 5.1 Neusoft Reach
• 5.2 SenseAuto
• 5.3 QCraft
• 5.4 DeepRoute.ai
• 5.5 Banma Intelligence
• 5.6 Thundersoft
• 5.7 Momenta
• 5.8 AFARI
• 5.9 ECARX
• Full-Stack Technology Product Matrix
• Multi-Domain Integration Products
• Zenith Central Computing Platform
• Antora(R) 1000 Plus Central Computing Platform
• Antora(R)1000 Pro Cockpit-Parking Integration Computing Platform
• Antora(R)1000 Central Computing Platform (1)
• Antora(R)1000 Central Computing Platform (2)
• Automotive Cerebrum (ECARX Super Brain) Central Computing Platform (1)
• Automotive Cerebrum (ECARX Super Brain) Central Computing Platform (2)
• Automotive Cerebrum (ECARX Super Brain) Central Computing Platform (3)
• Cross-Domain Software Solution (1)
• Cross-Domain Software Solution (2)
• 5.10 Autolink
• Cockpit-Driving Integration Platform Planning
• Cockpit-Driving Integration Platform (1)
• Cockpit-Driving Integration Platform (2)
• Cockpit-Driving Integration Platform (5)
• AI Computing Platform (1)
• AI Computing Platform (2)
• AI Supercomputing Architecture (1-4)
• 5.11 Hangsheng Electronics
• Cockpit-Driving Integration Platform (1)
• Cockpit-Driving Integration Platform (2)
• Cockpit-Driving Integration Platform (3)
• Apollo Computing Unit (AI)
• Full-Stack Hardware and Software Solution
• 5.12 BICV
• Cockpit-Driving Integration Platform Planning
• AI Box
• Cockpit-Driving Integration Platform
• 5.13 Lenovo Vehicle Computing
• AI Box
• Cockpit-Driving Integration Platform
• 5.14 Zhuoyu Technology
• 5.15 Modelbest Technology
• 5.16 Rivotek