Automotive Operating System and AIOS Integration Research Report, 2025
상품코드:1721399
리서치사:ResearchInChina
발행일:2025년 05월
페이지 정보:영문 540 Pages
라이선스 & 가격 (부가세 별도)
한글목차
차량용 OS와 AIOS의 관계
2023-2024년, 중앙 컴퓨팅 아키텍처의 부상으로 도메인 OS는 전체 도메인 소프트웨어 시스템의 통합을 담당하는 자동차 OS로 진화하기 시작했습니다.
2024년 하반기에는 AI 기반 모델이 대량 생산되어 자동차에 탑재되기 시작하고, 자동차 OS에 대한 새로운 요구사항이 생겨나며, 스케줄링 기능이 실현되어 자동차 AIOS의 채택이 더욱 촉진될 것입니다.
AIOS는 AI가 주도하는 OS로, OS에 "지능"을 부여하여 시스템이 스스로 최적화 및 의사결정을 할 수 있도록 하며, AIOS는 차량 지능의 최고봉으로, 복잡한 인지 데이터 처리, 지능형 의사결정 실행, 인간과 같은 상호 작용을 실현하는 역할을 합니다. 실행, 인간과 같은 상호 작용의 실현을 담당하며, 차량 OS는 모든 차량 기능의 소프트웨어 기반 역할을 합니다. 이 두 가지의 깊은 통합은 단순한 기능적 오버레이가 아니라 기본 아키텍처의 재구축, 산업 체인의 깊은 시너지, 경쟁 규칙의 재정의에 중요한 동력이 되고 있습니다.
1. Vehicle OS는 AI 기능 구현을 지원합니다 : 컴퓨팅 파워와 데이터를 제공할 뿐만 아니라, Vehicle OS의 SOA는 표준화된 인터페이스를 통해 차량 기능을 독립적인 서비스로 추상화하여 하드웨어와 소프트웨어의 분리를 실현합니다.
Geely를 예로 들면, Geely의 맞춤형 OS인 GOS는 SOA 개발 프레임워크를 기반으로 다양한 차량 기능을 서비스로 캡슐화하고, AI 기능이 이러한 서비스를 신속하게 호출하여 민첩한 개발 및 반복을 가능하게 하며, AI 기능의 신속한 배포와 지속적인 최적화를 위한 기반을 제공합니다. 2025년 초, Geely는 Full-Domain AI 시스템을 탑재하고, OS를 AIOS로 업그레이드하고, AIOS가 호출할 수 있는 모델 레이어를 설정했습니다.
2. AI가 차량용 OS를 재구성 : 기존 '기능 중심' 모델에서 보다 스마트한 '의도 중심' 모델로 전환
애플리케이션 계층의 AI 에이전트는 기본 모델의 의미 분석 능력을 활용하여 사용자의 자연어 명령과 잠재적 의도까지 정확하게 이해하고, 기본 소프트웨어 모듈을 자동으로 호출하여 작업을 완료할 수 있습니다. '의도 주도형' 상호작용 모델은 자동차가 능동적으로 사용자의 요구를 이해하고 서비스를 제공할 수 있도록 하여 사용자 경험을 보다 자연스럽고 편리하게 만들어 줍니다.
미들웨어(또는 모델) 계층의 기반 모델은 에이전트에게 호출 인터페이스를 제공할 뿐만 아니라 계획을 통해 차량 OS의 스케줄링 능력을 최적화합니다. 이 프로세스는 과거 데이터와 실시간 시스템 상태에 의존하고 강화 학습 및 운영 연구 알고리즘을 사용하여 시스템 리소스를 동적으로 할당하고 작업의 우선 순위를 정합니다. 예를 들어, 사용자가 내비게이션 계획과 고화질 비디오 재생을 동시에 시작할 때, 기반 모델은 경로 계산의 긴급도와 비디오 디코딩의 리소스 요구를 예측하고 CPU, GPU, NPU의 계산을 미리 조정하여 내비게이션의 응답과 원활한 비디오 재생을 모두 보장합니다. 기존 스케줄링 알고리즘의 리소스 경쟁으로 인한 끊김 현상을 방지합니다.
리소스 계층의 데이터는 양자의 가교 역할을 합니다. 차량 OS는 데이터 수집과 관리를 담당하고, AIOS는 데이터 분석과 의사결정을 담당합니다.
ArcherMind의 경우, 자회사인 Arraymo가 개발한 ArraymoAIOS 1.0은 온디바이스 AIOS로, 차량용 OS인 FusionOS 2.0과 함께 AIOS의 기술적 기반을 구성하고 있습니다.
본 보고서에서는 중국 자동차 산업을 조사하고, 자동차 OS의 AI 적용 현황과 동향을 설명하며, 자동차 OS와 AIOS가 어떻게 서로에게 힘을 실어주고 공진화할 수 있는지를 분석합니다.
목차
제1장 자동차용 AIOS 현황과 동향
AIOS 응용 배경
AIOS 아키텍처
다양한 산업의 터미널 AIOS 사례와 인사이트
AIOS 동향
제2장 자동차용 OS 개요
정의와 역사
자동차용 OS 동향
자동차용 OS 분류
소프트웨어 아키텍처
비즈니스 모델
카일렉트로닉스 규격 : AUTOSAR
카일렉트로닉스 규격 : OSEK
오픈 조직 : COVESA
제3장 기본적인 OS
BlackBerry
Linux, AGL
Android
Huawei
Alibaba
VxWorks
Ubuntu
webOS
ROS
제4장 하이퍼바이저
하이퍼바이저 소개
주요 하이퍼바이저의 비교
하이퍼바이저 산업 현황
하이퍼바이저 산업 현황 : 중국
하이퍼바이저 산업 현황 : 세계
세계의 자동차 하이퍼바이저 시장 전망
자동차 하이퍼 바이저 관리 시스템 비즈니스 모델
하이퍼바이저 비즈니스 모델(1)-(4)
QNX Hypervisor
ACRN
COQOS Hypervisor
PikeOS
EB Corbos Hypervisor
Harman Device Virtualization
VOSYSmonitor
Zlingsmart
제5장 범용 자동차용 OS와 기업
Neusoft Reach
Introduction to NeuSAR
Divide AIOS into Three Stages
Deployment of AI in Vehicle Intelligent OS
Four Layers of NeuSAR OS Architecture
NeuSAR SF(Service Framework) Middleware
NeuSAR AI Framework Middleware Products
NeuSAR Copilot Facilitates Efficient AUTOSAR Development
NeuSAR OS Completes DeepSeek Adaptation
NeuSAR aCore
Upgrades to AUTOSAR AP Products
NeuSAR cCore
Lightweight AUTOSAR CP Products
Collaboration with Infineon
ThunderSoft
AquaDrive OS Vehicle OS
Integration of Rubik Foundation Model with OS
AquaDrive OS Upgraded to AIOS
How AquaDrive OS Supports AI Function Implementation
How AquaDrive OS Supports AI Function Implementation : Cases
ArcherMind
Arraymo AIOS Base
Cross-Domain Vehicle OS : FusionOS 1.0
Cross-Domain Vehicle OS : FusionOS 2.0
Recent Dynamics
Kernelsoft
AI-Oriented Operating System Solutions
Real-Time Operating System
Linux
Operating System Security
Baidu
AI-Native Operating System : DuerOS X
AI-Native Operating System : Architecture
Integrated Vehicle OS Supply
iSOFT Infrastructure Software
AUTOSAR CP+AP Integrated Solutions(1)
AUTOSAR CP+AP Integrated Solutions(2)
CP Products
Vehicle OS Layout
Operating System Architecture
Vehicle Control OS : Open-Source EasyXMen
Intelligent Driving OS : EasyAda
ZTE GoldenOS
Microkernel and Macrokernel Technical Architecture
Vehicle Control OS Solution
Intelligent Cockpit OS Solution
Intelligent Driving OS Solution : Dual-Kernel Architecture
Intelligent Driving OS Solution : Application Scenarios
Intelligent Driving OS Solution : Evolution
Intelligent Driving OS Solution : Chip Adaptation
Dynamics in Neusoft Reach+ZTE+SemiDrive Cooperation
AICC
Product System
ICVOS : Intelligent Connected Vehicle OS
ICVOS : Software Architecture
ICVOS : Development Architecture
ICVOS : SDK Architecture
ICVOS : Platform-Based, Connected, Scalable
ICVOS : Vehicle-Cloud Cooperation
ICVOS : Information Security Foundation Platform
ICVOS : New Architecture for Autonomous Driving Domain
ICVOS : Cases of Software Architecture Co-development with OEMs(1)-(4)
NVIDIA DRIVE OS
Introduction to DRIVE OS
DRIVE OS SDK Architecture
EB
Tresos Real-Time Operating System
Tresos AutoCore Architecture
EB's J5-Based Intelligent Driving Domain OS
EB's Virtualization Development Technology
Other OS Vendors
STEP's Intelligent Driving OS Supports LLM and End-to-End Algorithm Deployment
iHUATEK Uses Large Vision Models to Build Vehicle OS
Freetech's SOA Structure Is Connected to Foundation Models
Research on automotive AI operating system (AIOS): from AI application and AI-driven to AI-native
Automotive Operating System and AIOS Integration Research Report, 2025, released by ResearchInChina, explains the status quo and trends of AI application in automotive operating systems (OS), and analyzes how vehicle OS and AIOS mutually empower and co-evolve.
The relationship between vehicle OS and AIOS:
From 2023 to 2024, with the rise of central computing architecture, domain operating systems started evolving towards vehicle OS which takes on integrating the full-domain software system.
In the second half of 2024, AI foundation models started being mass-produced and introduced into vehicles, which raises new requirements for vehicle operating systems and also enables their scheduling capabilities, further facilitating the adoption of automotive AIOS.
AIOS is an AI-driven operating system that enables operating systems with "intelligence", that is, allow the systems to independently make optimizations and decisions during task execution and scheduling. AIOS represents the pinnacle of vehicle intelligence, and is responsible for handling complex perceptual data, executing intelligent decision, and realizing human-like interaction, while vehicle OS serves as the software foundation for all vehicle functions. The deep integration of the two is not merely a functional overlay but a key force driving reshaping of underlying architecture, deep synergy in industry chain, and redefinition of competitive rules.
1.Vehicle OS supports the implementation of AI capabilities: Beyond providing computing power and data, the SOA of vehicle OS abstracts vehicle functions into independent services through standardized interfaces, achieving hardware-software decoupling, and makes it easy to call interfaces across different software modules through atomic services, providing a stable and flexible invocation environment for AI models. Take Geely as an example:
Geely's customized OS, GOS, is based on an SOA development framework that encapsulates various vehicle functions as services and allows AI functions to quickly call these services for agile development and iteration, providing the foundation for the rapid deployment and continuous optimization of AI capabilities. In early 2025, Geely introduced its "Full-Domain AI" system, and upgraded its OS to AIOS, with a model layer set up for AIOS to call.
2.AI reconstructs vehicle OS: Shifting it from the traditional "function-driven" model to a smarter "intent-driven" model:
AI Agents at the application layer can leverage foundation models' semantic analysis capabilities to accurately understand users' natural language commands and even latent intentions, and automatically invoke underlying software modules to complete tasks. The "intent-driven" interaction model is used to enable vehicles to proactively understand needs and provide services, making user experience much more natural and convenient.
Foundation models at the middleware (or model) layer not only provide calling interfaces for agents but also optimize the scheduling capabilities of vehicle OS through planning. This process relies on historical data and real-time system states, and uses reinforcement learning and operations research algorithms to dynamically allocate system resources and prioritize tasks. For instance, when a user simultaneously initiates navigation planning and high-definition video playback, foundation models can predict the urgency of route calculation and the resource demands of video decoding, coordinate CPU, GPU, and NPU compute in advance to ensure both navigation response and smooth video playback, avoiding stuttering caused by resource contention in traditional scheduling algorithms.
Data at the resource layer serves as the bridge between the two. Vehicle OS is responsible for data collection and management, while AIOS handles data analysis and decision-making.
In ArcherMind's case, its subsidiary Arraymo developed ArraymoAIOS 1.0, an on-device AI operating system which, together with the vehicle operating system FusionOS 2.0, constitutes the technical base of AIOS. Key features of this base include:
Support use of Qualcomm SA8775P to build cockpit agents, and NVIDIA Orin to build vehicle agents, each equipped with 10+ deeply optimized on-device models (DeepSeek, Llama, Baichuan, Gemma, Yi-Chat, etc.).
Introduce intelligent scheduling algorithms to monitor and analyze multi-modal task loads (text, image, audio, etc.) in real time, and dynamically adjust the strategies for allocation of resources like CPU, GPU, and memory.
Introduce the AI acceleration engine AMLightning to efficiently schedule computing units in AI chips, allowing reasoning tasks to run on the most suitable computing unit.
Evolution of AIOS: From AI Application and AI-Driven to AI-Native
In the automotive sector, AI was initially integrated at the application layer of the operating system, invoked via interfaces for specific scenarios. Entering the era of AIOS, AI starts penetrating deeper into the underlying layer, from being integrated into the middleware layer for driving functions, to touching the OS kernel and underlying architecture. In the future, it will evolve into AI-native OS.
As of April 2025, there have been three modes of AI integration in OS, corresponding to the three development phases of AIOS:
AI Application Phase: introduced as applications to serve scenarios.
AI-Driven Phase: connected at the middleware layer, utilizing components like AI Runtime and AI frameworks (models/agents/algorithm frameworks) to drive various software functions more flexibly.
AI-Native Phase: large language models (LLMs) are called as microkernel modular components, providing platform-level AI capabilities for the entire OS.
Huawei believes that the application of AI technology in terminal products typically passes through three phases: AI integration at the application layer, AI fusion at the system layer, and AI-centric new OS.
As of H1 2025, most OEMs have already deployed AI at the application layer and have begun to integrate AI components into the middleware layer. Examples include Li Auto's Halo OS, NIO's Sky OS, Xiaomi's Hyper OS, and Geely's AIOS GOS.
AI Application Phase
At this phase, AI is integrated into the application layer of OS to be called for scenarios. OS primarily provides computing power and data interfaces to optimize and upgrade basic AI functions like navigation and voice interaction. For example, in a "vehicle assistant" scenario, when a user calls AI for car-related knowledge, AI at the application layer first analyzes the request, converts it into a command, retrieves relevant data from databases, and formulates a natural-language answer displayed on the center console screen.
AI-Driven Phase
At this phase, AIOS extends into the middleware layer, becoming a mainstream approach for AI Agent invocation in intelligent cockpits. Upper-layer agents leverage AI components to directly call SOA atomic services via framework modules to control vehicle functions or other software features. Additionally, toolchains can be used to call multiple external tools and ecological interfaces to achieve "touchless" automation for scenarios.
For instance, the "people search by photographing" function of Li Auto's MindVLA requires MindVLA to successively complete such steps as object recognition, map data matching, and route planning, involving use of components like AI reasoning framework and reasoning acceleration, and invocation of external maps and location data.
Li Auto's Halo OS incorporates an AI subsystem in the middleware layer, which includes not only AI Runtime but also components like AI reasoning engine and reasoning acceleration framework.
AI-Native Phase
AI-Native refers to systems or product forms that are fundamentally driven by AI, and deeply integrate AI in design from the ground up.
An AI-Native OS is an operating system that deeply integrates AI into its underlying architecture from the beginning of design, features system-level AI capabilities, and delivers all-scenario intelligent experience and rich agent ecosystems.
When AI and OS achieve deep integration, an AI-Native OS is formed. The system can intelligently optimize resource allocation and task scheduling according to application scenarios and demands, thus bringing a qualitative leap in overall efficiency and intelligence, rather than merely taking AI as an upper-layer application or functional module.
In Huawei's case, its AI-Native OS has the following features:
Unified AI system base
AI-Native applications
Xiaoyi Super Agent
Open ecosystems
Underpinned by the AI system base, super apps/agents are built and rich ecosystems are created. AI-native HarmonyOS features multimodal understanding, personalized user data understanding, and privacy protection capabilities, and all-scenario perception and collaboration capabilities.
In April 2025, Huawei launched HarmonySpace 5, a HarmonyOS-based cockpit which adopts the MoLA hybrid foundation model architecture. It leverages a multi-model base (including DeepSeek), led by the PanGu Models, to enable system agent and vertical agent scenario applications. The entire upper-layer applications are supported by the system-level AI capabilities of HarmonyOS 5.0.
In ThunderSoft's case, in 2025, AquaDrive OS has been upgraded to an AI-native OS, offering optimizations in the following directions:
The AI middleware of AquaDrive OS includes agent perception/execution services and an agent management framework to support multi-agent interaction. It also incorporates a foundation model inference and scheduling framework, supporting connection to various cloud and on-device foundation models to achieve life-oriented multimodal recognition and environmental guidance.
Its framework provides SOA services, and enables modular software function calls with atomized support.
Table of Contents
Definitions
1 Status Quo and Trends of Automotive AIOS
1.1 Application Background of AIOS
Application Background of Vehicle OS in the AI Era
Requirements for Vehicle OS in the AI Era (1) - (3)
Overview of AI Application in Automotive OS
1.2 AIOS Architecture
Construction Methods of LLM OS
AIOS Architecture: Main Components and Functions of Kernel Module (1) - (7)
AIOS Architecture: Throughput and Latency/Performance Maintenance in Parallel State
AIOS Architecture: Agent Structure
AIOS Architecture: Model Deployment and Task Flow
AIOS Architecture: Definition and Characteristics of AI Runtime
AIOS Architecture: Comparison between Different AI Runtimes
