Dublin, May 14, 2024 (GLOBE NEWSWIRE) -- The "AI Foundation Models' Impacts on Vehicle Intelligent Design and Development Research Report, 2024" report has been added to ResearchAndMarkets.com's offering.
How foundation models will rebuild intelligent vehicles?
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.
Will foundation model/AI Agent replace OS/APP?
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.
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.
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.
Key Topics Covered:
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
1.4 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
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
2.2 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
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
3.2 Impacts of AI Foundation Models on OS Design and Development
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
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
5.3 Impacts of AI Foundation Models on Cockpit Domain Controller
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
For more information about this report visit https://www.researchandmarkets.com/r/bgr0x
About ResearchAndMarkets.com
ResearchAndMarkets.com is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.
