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市场调查报告书
商品编码
1457877

基于人工智慧的模型对车辆智慧设计的影响与发展（2024）

AI Foundation Models' Impacts on Vehicle Intelligent Design and Development Research Report, 2024
出版日期: | 出版商: ResearchInChina | 英文 160 Pages | 商品交期: 最快1-2个工作天内

价格
简介目录

基于人工智慧的模型正在蓬勃发展。ChapGPT和SORA的出现让人震惊。AI前沿的科学家和企业家指出，基于AI的模型将重塑生活的方方面面，尤其是科技相关领域。智慧汽车作为科技产品，基于AI的车型将带来怎样的改变？

基础模型将如何重建智慧汽车

2023年，Changan Automobile在其专有的软体驱动架构（SDA）中添加了人工智慧边缘和人工智慧服务层，其中包括L1-L6层。AI技术已被证明影响智慧汽车的大部分层，如L3 EEA层、L4车辆操作系统层、L6车辆功能应用层（包括座舱、连接和智慧驾驶）以及我理解的L7云大数据层。L1机械层的底盘部分和L2动力层的电池部分其实都涉及AI应用。

如今，OEM和Tier 1依赖基础模型来实现其部分车辆智慧或作为其开发过程的一部分的连结。

在审视人工智慧模型在汽车领域的整体应用趋势的同时，也需要关注基础模型的演进。根据Tencent Research Institute的研究结果，人工智慧将从大脑进化到AI Agent，从副驾驶进化到自动驾驶。

那什么是AI代理呢？

基础模型/AI Agent会取代OS/APP吗？

ResearchInChina接受以下观点：AI基础模型是OS，AI Agent是应用程式。智慧产品的开发典范将从传统的OS-APP生态范式转变为AI基础模型-AI Agent生态典范。

AI Agent 是一个超越简单文字生成的 AI 系统。AI Agent 使用大规模语言模型 (LLM) 作为核心计算引擎，可进行对话、执行任务、推理并具有一定程度的自主权。换句话说，AI Agent 是一个具有复杂推理能力、记忆体和执行任务方式的系统。由此可见，NIO座舱内安装的NOMI GPT和TeslaFSD V12分别是座舱域和智慧驾驶域的AI Agent。

AI基础模型是平台级的AI技术，包括ChatGPT、ERNIE Bot等领先科技公司推出的模型。平台级人工智慧是为作业系统各个面向提供动力的技术基础。这被认为是下一代作业系统的新核心。传统作业系统中的核心主要负责管理和调度系统的硬体资源，如GPU、记忆体等，以确保系统的正常运作和高效利用。然而，随着用户需求的增加，人工智慧系统将需要解析许多与人类相关的个人化体验。

传统作业系统无法有效计算或处理个人知识库、对人们位置和状态的感知、人们的习惯和爱好以及其他个人化因素。因此，需要一个全新的核心来满足这些要求。平台级人工智慧模型的优点在于它可以管理和处理多种个人因素，并允许作业系统准确识别使用者意图。这样的特性让新作业系统能够带给每个人 "猜你想要什么，懂你需要什么" 的智慧体验。

本报告对中国汽车产业进行了调查和分析，提供了人工智慧模型的现状和未来趋势、对汽车设计的影响以及应用实例。

目录

第一章 人工智慧模型的现况与未来趋势

  • 基于AI的模型应用介绍
  • 目前使用情况
  • Sora，文字转影片转换的基础模型
  • 概括

第二章 AI基础模型对车辆硬体层的影响

  • 人工智慧基础模型对晶片设计和功能的影响
  • 基于 AI 的模型对 ADAS 感测器和识别系统开发的影响

第三章 AI基础模型对汽车SOA/作业系统的影响

  • AI 基础模型对 SOA/EE 架构的影响
  • 基于人工智慧的模型对作业系统设计和开发的影响

第4章 以人工智慧为基础的模型对汽车资料闭环/模拟系统的影响

  • 基于人工智慧的模型对资料闭环的影响
  • 基于人工智慧的模型对模拟系统的影响

第五章 AI模型对自动驾驶/智慧座舱的影响

  • 基于人工智慧的模型对自动驾驶的影响
  • AI模型在自动驾驶的应用实例
  • AI基础模型对座舱域控制器的影响

第 6 章 AI Agent 和汽车

  • 什么是AI代理？
  • AI Agent发展方向
  • 智慧汽车AI Agent应用趋势
  • AI Agent在车辆上的应用范例
简介目录
Product Code: GX010

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.

Table of Contents

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

6 AI Agent and Automobile

  • 6.1 What is AI Agent
  • 6.2 Development Directions of AI Agent
  • 6.3 Application Trends of AI Agent for Intelligent Vehicles
  • 6.4 Application Cases of AI Agent in Vehicles