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1457877

基於人工智慧的模型對車輛智慧設計的影響與發展（2024）

AI Foundation Models' Impacts on Vehicle Intelligent Design and Development Research Report, 2024

出版日期: 2024年03月10日 | 出版商:

ResearchInChina | 英文 160 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

基於人工智慧的模型正在蓬勃發展。ChapGPT和SORA的出現讓人震驚。AI前沿的科學家和企業家指出，基於AI的模型將重塑生活的方方面面，尤其是科技相關領域。智慧汽車作為科技產品，基於AI的車型將帶來怎樣的改變？

基礎模型將如何重建智慧汽車

2023年，Changan Automobile在其專有的軟體驅動架構（SDA）中添加了人工智慧邊緣和人工智慧服務層，其中包括L1-L6層。AI技術已被證明影響智慧汽車的大部分層，如L3 EEA層、L4車輛操作系統層、L6車輛功能應用層（包括座艙、連接和智慧駕駛）以及我理解的L7雲大數據層。L1機械層的底盤部分和L2動力層的電池部分其實都涉及AI應用。

如今，OEM和Tier 1依賴基礎模型來實現其部分車輛智慧或作為其開發過程的一部分的連結。

在審視人工智慧模型在汽車領域的整體應用趨勢的同時，也需要關注基礎模型的演進。根據Tencent Research Institute的研究結果，人工智慧將從大腦進化到AI Agent，從副駕駛進化到自動駕駛。

那什麼是AI代理呢？

基礎模型/AI Agent會取代OS/APP嗎？

ResearchInChina接受以下觀點：AI基礎模型是OS，AI Agent是應用程式。智慧產品的開發典範將從傳統的OS-APP生態範式轉變為AI基礎模型-AI Agent生態典範。

AI Agent 是一個超越簡單文字生成的 AI 系統。AI Agent 使用大規模語言模型 (LLM) 作為核心計算引擎，可進行對話、執行任務、推理並具有一定程度的自主權。換句話說，AI Agent 是一個具有複雜推理能力、記憶體和執行任務方式的系統。由此可見，NIO座艙內安裝的NOMI GPT和TeslaFSD V12分別是座艙域和智慧駕駛域的AI Agent。

AI基礎模型是平台級的AI技術，包括ChatGPT、ERNIE Bot等領先科技公司推出的模型。平台級人工智慧是為作業系統各個面向提供動力的技術基礎。這被認為是下一代作業系統的新核心。傳統作業系統中的核心主要負責管理和調度系統的硬體資源，如GPU、記憶體等，以確保系統的正常運作和高效利用。然而，隨著用戶需求的增加，人工智慧系統將需要解析許多與人類相關的個人化體驗。

傳統作業系統無法有效計算或處理個人知識庫、對人們位置和狀態的感知、人們的習慣和愛好以及其他個人化因素。因此，需要一個全新的核心來滿足這些要求。平台級人工智慧模型的優點在於它可以管理和處理多種個人因素，並允許作業系統準確識別使用者意圖。這樣的特性讓新作業系統能夠帶給每個人 "猜你想要什麼，懂你需要什麼" 的智慧體驗。

本報告對中國汽車產業進行了調查和分析，提供了人工智慧模型的現狀和未來趨勢、對汽車設計的影響以及應用實例。

AI foundation models are booming. The launch of ChapGPT and SORA is shocking. Scientists and entrepreneurs at AI frontier point out that AI foundation models will rebuild all walks of life, especially tech-related fields. As a technological product, how will intelligent vehicles be changed by AI foundation models?

How foundation models will rebuild intelligent vehicles?

Following the "Automotive AI Foundation Model Technology and Application Trends Report, 2023-2024", a report which discusses impacts of AI foundation models on automotive industry from a macro perspective, ResearchInChina released the "AI Foundation Models' Impacts on Vehicle Intelligent Design and Development Research Report, 2024", the second report which researches the impacts of AI foundation models on vehicle intelligent design and development in the such aspects as hardware, operating system, application function, and cloud big data.

In 2023, Changan Automobile added AI edge and AI service layer to the original software-driven architecture (SDA) that includes L1-L6 layers. It can be seen that AI technology has affected most layers of intelligent vehicles: L3 EEA layer, L4 vehicle OS layer, L6 vehicle function application layer (including cockpit, connectivity and intelligent driving), L7 cloud big data layer, etc. The chassis part of L1 mechanical layer and the battery part of L2 power layer have actually involved AI application.

Currently, OEMs and Tier1s apply foundation models to part of vehicle intelligence, or to some link in the development process.

When viewing the general application trend of AI foundation models in vehicles, we also need to find an idea in the evolution of foundation models. According to the results of Tencent Research Institute, AI will evolve from the brain to AI Agent, and from CoPilot to autonomous driving.

So, what is AI Agent?

Will foundation model/AI Agent replace OS/APP?

ResearchInChina accepts the view: AI foundation model is the OS, and AI Agent is the application. The development paradigm of intelligent products will be changed from conventional OS-APP ecosystem paradigm to AI foundation model-AI Agent ecosystem paradigm.

What is AI Agent? It is an artificial intelligence (AI) system beyond simple text generation. AI Agent uses a large language model (LLM) as its core computing engine, so that it can make conversations, perform tasks, make inferences, and have a degree of autonomy. In short, AI Agent is a system with complex reasoning capabilities, memory and task execution methods. It is thus clear that NOMI GPT in NIO's cockpit and Tesla FSD V12 are AI Agents in the cockpit domain and intelligent driving domain, respectively.

AI foundation models, a platform-level AI technology, include those launched by first-tier technology companies, such as ChatGPT and ERNIE Bot. Platform-level AI can serve as the technological foundation to empower operating systems in all aspects. It is regarded as the new kernel of next-generation operating systems. The kernel of conventional operating systems is mainly responsible for managing and scheduling the system's hardware resources like GPU and memory to ensure normal operation and efficient utilization of system. Yet with increasing user demand, AI systems need to parse many human-related personalized experiences.

For personal knowledge base, people's location and status awareness, people's habits and hobbies and other personalization factors, conventional operating systems fall short of effective calculation and processing. We thus need a brand-new kernel to meet these requirements. The strength of platform-level AI foundation models is that they can manage and process multiple personal factors and help the operating system accurately recognize user intents. With such capabilities, fire-new operating systems can bring everyone an intelligent experience of "guess what you want and understand what you need."

In automotive cockpit applications, to achieve true personalization, automakers also need to further customize the AI foundation model according to the features of their own vehicle models and services, that is, AI Agent based on platform-level AI foundation model. We can see that Geely models (such as Jiyue and Galaxy) are based on Baidu ERNIE Bot-based cockpit systems, and Mercedes-Benz's in-car voice assistant are actually an AI Agent after being connected to ChatGPT.

At present, intelligent driving AI Agent and cockpit AI Agent are separate. As cockpit-driving integration develops, they will tend to be integrated. However when considering cockpit-driving integration, OEMs and Tier1s cannot only consider integration at the hardware level, but also need to take into account operating system and vehicle system architecture, especially rapid evolution of foundation models/AI Agent models.

Foundation model/AI Agent is currently a part of an operating system/APP ecosystem. Will it replace operating systems/APP models in the future? We think it's possible.

Foundation model-based agents will not only allow everyone to have an exclusive intelligent assistant with enhanced capabilities, but also change the mode of human-machine cooperation and bring broader human-machine fusion. There are three human-AI cooperation modes: Embedding, Copilot, and Agent.

In intelligent driving, the Embedding mode is equivalent to L1-L2 autonomous driving; the Copilot mode, L2.5 and highway NOA; the Agent mode, urban NOA and L3 autonomous driving.

In the Agent mode, humans set goals and provide necessary resources (e.g., computing power), then AI independently undertakes most of tasks, and finally humans supervise the process and evaluate the final results. In this mode, AI fully embodies the interactive, autonomous and adaptable characteristics of Agents and is close to an independent actor, while humans play more of a supervisor and evaluator role.

A large number of interactive operations that were originally enabled via IVI APP can now be achieved through natural interactions (voice, gesture, etc.) in the AI Agent mode. AI Agent even actively observes the inside and outside of the vehicle, makes a request inquiry, and can perform a task after being confirmed by the user.

Therefore, the development of AI Agent is bound to make a mass of previous apps unnecessary and will have a disruptive impact on the development and application of intelligent cockpit and intelligent driving.

The current AI foundation models are not an operating system, but a paradigm and architecture of AI models, focusing on how to enable machines to process multimodal data (text, image, video, etc.). AI Agent is more similar to an AI application or application layer, which requires the support of the underlying operating system and hardware for operation. It is not in itself responsible for the basic management and resource scheduling of the computer system. In the future, AI foundation models are likely to be combined with OS to become AIOS.

AI foundation models and AI Agent development have the following impacts on future operating systems:

Applets will disappear or evolve into AI Agent that calls foundation models;

OS may evolve into the foundation model + computing chip core cluster OS architecture;

AI foundation models as a platform redefine and empower all kinds of industrial application scenarios, and give rise to more human-computer interaction-centric native applications, including autonomous vehicles, robots and digital twin applications.

1 Current Application and Future Trends of AI Foundation Models

1.1 Introduction to AI Foundation Model Application
- 1.1.1 Introduction to Various Types of AI Models
- 1.1.2 Multimodal Foundation Model VLM: Generic Architecture and Evolution Trends
- 1.1.3 Evolution Trends of Foundation Models Understanding 3D Road Scenarios
- 1.1.4 Summary of Evolution Trends of Multimodal Foundation Models Understanding Intelligent Vehicle Driving Road Scenarios
1.2 Current Application
- 1.2.1 Classification of AI Foundation Model Applications
- 1.2.2 Current Application of AI Foundation Models: Suppliers
- 1.2.3 Current Application of AI Foundation Models: OEMs
- 1.2.4 Application of AI Foundation Models in Different Vehicle Layers
- 1.2.5 Application Cases of AI Foundation Models in Different Scenarios
1.3 Sora Text-to-Video Foundation Model
- 1.3.1 Autonomous Driving (AD) Foundation Model: World Model and Video Generation
- 1.3.2 Visual Foundation Model: Historical Review and Comparative Analysis
- 1.3.3 Sora: Fundamental and Social Value
- 1.3.4 Sora: Introduction to the Basic System
- 1.3.5 Sora: Basic Functions
- 1.3.6 Sora: Advantages and Limitations
- 1.3.7 Sora: Case Studies
- 1.3.8 Interpretation of Sora Module (1)
- 1.3.9 Interpretation of Sora Module (2)
- 1.3.10 Interpretation of Sora Module (3)
- 1.3.11 Interpretation of Sora Module (4)
- 1.3.12 Sora vs GPT-4: Comparative Analysis of Computing Power
- 1.3.13 Sora: Prediction for How to Drive Autonomous Driving Industry
1.4 Summary
- 1.4.1 AI Foundation Models Lead to Emergence Effects
- 1.4.2 Advantages of AI Foundation Models over Conventional AD Models
- 1.4.3 Impacts of AI Foundation Models on Operating Systems
- 1.4.4 Impacts of AI Foundation models on SOA/Simulation Design/SoC Design
- 1.4.5 Impacts of AI Foundation Models on Autonomous Driving Development
- 1.4.6 AI Foundation Model Evolution Trend 1
- 1.4.7 AI Foundation Model Evolution Trend 2
- 1.4.8 Enduring Problems of AI Foundation Models in Intelligent Vehicle Industry and Solutions
- 1.4.9 Existing Problems of AI Foundation Models
- 1.4.10 Impacts of Sora on Intelligent Vehicle Industry and Prediction
- 1.4.11 Enduring Problems in AI Computing Chip Design and Solutions
- 1.4.12 AI Foundation Model: New Breakthroughs in Human-Machine Fusion Decision & Control
- 1.4.13 Summary of AI Foundation Models' Impacts on Vehicle Intelligence (1)
- 1.4.14 Summary of AI Foundation Models' Impacts on Vehicle Intelligence (2)
- 1.4.15 Summary of AI Foundation Models' Impacts on Vehicle Intelligence (3)
- 1.4.16 Summary of AI Foundation Models' Impacts on Vehicle Intelligence (4)
- 1.4.17 Summary of AI Foundation Models' Impacts on Vehicle Intelligence (5)
- 1.4.18 Summary of AI Foundation Models' Impacts on Vehicle Intelligence (6)

2 Impacts of AI Foundation Models on Vehicle Hardware Layer

2.1 Impacts of AI Foundation Models on Chip Design and Functions
- 2.1.1 Impact Trends of AI Foundation Models on Chips (1)
- 2.1.2 Impact Trends of AI Foundation Models on Chips (2)
- 2.1.3 Impact Trends of AI Foundation Models on Chips (3)
- 2.1.4 Changes LLM Makes to Intelligent Vehicle SoC Design Paradigm
- 2.1.5 Case 1
- 2.1.6 Case 2
- 2.1.7 NVIDIA's DRIVE Family Chips for Autonomous Driving
- 2.1.8 Case 3
- 2.1.9 Impacts of AI Foundation Models on Cockpit Chip Design and Planning
- 2.1.10 Case 4
2.2 Impacts of AI Foundation Models on ADAS Sensor and Perception System Development
- 2.2.1 Foundation Model-Driven: Evolution Trends of Perception Capability Fusion and Sharing
- 2.2.2 Case 5
- 2.2.3 Case 6

3 Impacts of AI Foundation Models on Automotive SOA/Operating System

3.1 Impacts of AI Foundation Models on SOA/EE Architecture
- 3.1.1 Driving Factors for EEA Evolution
- 3.1.2 AI Foundation Model's Requirements for Computing Power Also Drive EEA Evolution
- 3.1.3 Multimodal Foundation Model and EEA 3.0
- 3.1.4 Development Directions of SOA in Terms of Foundation Model Agent Technology
- 3.1.5 Case 1
3.2 Impacts of AI Foundation Models on OS Design and Development
- 3.2.1 How AI Foundation Model Affects OS (1)
- 3.2.2 How AI Foundation Model Affects OS (2)
- 3.2.3 How AI Foundation Model Affects OS (3)
- 3.2.4 Case 2
- 3.2.5 Case 3
- 3.2.6 Case 4
- 3.2.7 Case 5
- 3.2.8 Case 6

4 Impacts of AI Foundation Models on Automotive Data Closed Loop/Simulation System

4.1 Impacts of AI Foundation Models on Data Closed Loop
- 4.1.1 Data-driven Autonomous Driving System
- 4.1.2 Data-driven and Data Closed Loop
- 4.1.3 Application of Foundation Models in Intelligent Driving
- 4.1.4 Changan's Data Closed Loop
- 4.1.5 Dotrust Technologies' Cloud Data Closed Loop Solution SimCycle
- 4.1.6 Huawei's Pangu Model and Data Closed Loop
- 4.1.7 How Huawei Pangu Model Enables Autonomous Driving Development Platforms
- 4.1.8 SenseTime's Data Closed Loop Solution
- 4.1.9 Juefx Technology Uses Horizon Robotics' Chips and Foundation Model to Complete Data Closed Loop
4.2 Impacts of AI Foundation Models on Simulation System
- 4.2.1 Autonomous Driving Vision Foundation Model (VFM)
- 4.2.2 Comparative Analysis of Sora and Tesla FSD-GWM
- 4.2.3 Comparison between Sora and LLM
- 4.2.4 Comparison between Sora and ChatSim
- 4.2.5 Multimodal Basic Foundation Model
- 4.2.6 Generative World Model GAIA-1 System Architecture
- 4.2.7 Case 1
- 4.2.8 Case 2
- 4.2.9 Case 3
- 4.2.10 Case 4

5 Impacts of AI Foundation Models on Autonomous Driving/Intelligent Cockpit

5.1 Impacts of AI Foundation Models on Autonomous Driving
- 5.1.1 AD Foundation Model: Application Scenarios and Strategic Significance
- 5.1.2 AD Foundation Model: Typical Applications
- 5.1.3 AD Foundation Model: Typical Applications and Limitations
- 5.1.4 AD Foundation Model: Main Adaptation Scenarios and Application Modes
- 5.1.5 VLM/MLM/VFM: Industrial Adaptation Scenarios and Main Applications
- 5.1.6 AD Foundation Model: Adaptation Scenarios Case
- 5.1.7 AD Vision Foundation Model: Data Representation and Main Applications
- 5.1.8 Evolution Trends of Intelligent Driving Domain Controller
- 5.1.9 Application of Multimodal Foundation Model in Intelligent Driving
5.2 Application Cases of AI Foundation Model in Autonomous Driving
- 5.2.1 Case 1
- 5.2.2 Case 2
- 5.2.3 Case 3
- 5.2.4 SenseTime Drive-MLM: World Model Construction
- 5.2.5 SenseTime Drive-MLM: Multimodal Generative Interaction
- 5.2.6 Case 4
- 5.2.7 Case 5
- 5.2.8 Case 6
- 5.2.9 Qualcomm Hybrid AI: Application in Intelligent Driving
- 5.2.10 Qualcomm AI Model Library
- 5.2.11 Case 7
- 5.2.12 Case 8
5.3 Impacts of AI Foundation Models on Cockpit Domain Controller
- 5.3.1 Multimodal Foundation Model
- 5.3.2 Impacts of Foundation Models on Interaction Design: Data Analysis and Decision
- 5.3.3 Impacts of Foundation Models on Interaction Design: Personalization through Autonomous Learning
- 5.3.4 Case 1
- 5.3.5 Case 2
- 5.3.6 Case 3
- 5.3.7 Case 4
- 5.3.8 Case 5