Global and China Software-defined Vehicle (SDV) Market Report, 2023-2024 – Key Developments in OS Kernels, Cloud-Native Platforms, and Open-Source Initiatives Driving Innovation – ResearchAndMarkets.com

DUBLIN–(BUSINESS WIRE)–The “Software-defined Vehicle Research Report, 2023-2024 – Industry Panorama and Strategy” report has been added to ResearchAndMarkets.com’s offering.

This report explores the development of intelligent driving and cockpit software systems. It begins with the architecture of intelligent driving systems, including real-time vehicle control operating systems, middleware like ROS and AutoSAR, and strategies for building generalized OS for autonomous driving. It also covers universal algorithms, AI deep learning platforms, data training sets, and system integration strategies.

The report delves into terminal-cloud integration for intelligent driving, focusing on data closed-loop processes, collection and annotation, simulation testing, and cloud-native storage solutions. It also discusses the role of HD maps and ADAS in performance evaluation and data recording.

The section on intelligent cockpit systems examines software and hardware architecture, automotive non-RTOS, and intelligent cockpit operating systems. It includes the use of hypervisors and application algorithms like GPT models, UI design, voice recognition, and acoustics software.

Building Intelligent Driving Software-Defined Vehicle (SDV) Architecture

The autonomous driving intelligent platform comprises four main components:

1. Hardware Platform
2. System Software: Includes hardware abstraction layer, OS kernel, and middleware.
3. Functional Software: Comprises library components and middleware.
4. Application Algorithm Software: Encompasses autonomous driving and HMI.

R&D Links in Autonomous Driving Basic Software for Intelligent Driving

• Real-time vehicle control OS (narrowly defined OS)
• Intelligent driving middleware (e.g., ROS, CyberRT, DDS, AutoSAR)
• Autonomous driving OS (broadly defined OS)

General Algorithm Design for Intelligent Driving

• Positioning, perception, planning, decision-making algorithms
• Models such as BEV Transformer, Occupancy Network, and end-to-end neural networks

General Algorithm Training for Intelligent Driving

• AI deep learning software platforms
• Intelligent driving data training sets

Terminal-Cloud Integration

• Data closed-loop, data collection, labeling
• Simulation testing (scene library, simulation platform)
• Cloud-native platforms, HD maps

System Integration and Engineering Implementation

• Features like FCW, LDW, ALC, APA/AVP

Intelligent Driving Assistance Software

• ADAS performance evaluation, data recording

Hardware Engineering

• Domain controllers (chips, hardware engineering)
• Sensors (LiDAR, radar, ultrasonic radar, cameras, GNSS, IMU)
• System engineering, chassis-by-wire, brake-by-wire

Hardware System Design

• Computing platform hardware system architecture
• Vehicle chip and sensor system design

Development Paths for Intelligent Driving OS Kernels

1. Linux-based Path

   • Enhancing Linux for security and real-time performance
   • Developing ASIL-B/D-compliant Safety Linux
   • Rich ecosystems but challenging safety certification

2. Microkernel RTOS Path

   • Emphasizing functional safety (targeting ASIL-D)
   • Implementing microkernel RTOS (e.g., QNX OS)
   • Lacking open-source ecosystem support, difficult development

Safety Linux is emerging as a significant OS in China, with major developments from companies like ZTE and Banma.

Key Players and Projects ZTE

• Dual-core intelligent driving OS: ZTE Microkernel RTOS, ZTE Hypervisor, ZTE Safety Linux
• Collaborations with Neusoft Reach, iSOFT, Horizon Robotics, Black Sesame Technologies, and SemiDrive
• Projects with Changan Automobile, FAW, and Dongfeng

Banma AliOS Drive

• Dual-core driver: AliOS RTOS and AliOS Safety Linux
• Collaborations with over 10 mainstream chip vendors
• Supports Horizon Journey 5 chip

Localization and Open Source Efforts

China’s localization rate for vehicle operating systems is about 5-10%. Efforts include:

• ZTE’s microkernel OS with Black Sesame Technologies
• Banma Zhixing’s partnerships and Horizon Journey 5 chip support
• iSOFT’s open-source initiatives with CAAM and various automakers
• Kernelsoft’s Photon RTOS supporting chips like NVIDIA Orin and Black Sesame A1000

Automaker Initiatives Tesla

• Custom RTOS based on Linux for domain controllers and FSD SoC

Li Auto

• Customized Linux kernel for Li OS on all-electric models and self-developed SoC

NIO

• SkyOS based on Linux for NT3.0 platform models, adapted to various chip platforms

Intelligent Cockpit Architecture R&D Links

1. Cockpit Basic Software

   • Vehicle operating systems (e.g., QNX, Linux, Android, HarmonyOS, AliOS)
   • Virtual machines (Hypervisor), middleware (AutoSAR)

2. System Software Development

   • Application and cluster software development based on various OS
   • TBOX software development

3. Cockpit Interface Design

   • UI design software

4. Cockpit Application Software

   • User portrait, situational awareness, multimodal interaction (e.g., AR HUD, voice, DMS/OMS, face and gesture recognition)

5. Cloud Services

   • Vehicle-cloud integrated platforms, cloud-native platforms, information security, OTA development

Cloud-Native Platform Developments

1. Self-Development

   • Ensuring independent and controllable IT R&D and platform construction

2. Open Source

   • Emphasizing open-source technologies for cloud-native platforms

3. Comprehensive Digital Transformation

   • Utilizing cloud-native technologies for digital transformation

Examples of Cloud-Native Implementations

• Geely: Cloud-native technology with Volcengine and Neusoft Reach
• SAIC Motor Passenger Vehicle: CloudOS for vehicle-cloud data cooperation
• Aptiv: Cloud-native DevOps platform with Wind River

Intelligent Vehicle Control Architecture

Involves body control, chassis control, power control, and energy management, evolving towards a centralized “central computing + zone controllers” architecture.

Examples of Implementations

• Lotus VMCU: Integrates vehicle-level control functions, including TVC, ESP, and TCS.
• UAES USP 2.0: Developer platform for integrating multiple ECUs via zonal architecture.
• Continental’s FaaP: Decouples software and hardware for body and actuator functions.

Key Topics Covered

1 How to Build Intelligent Driving Software System?
1.1 Overall Software and Hardware Architecture of Intelligent Cockpit

1.2 Basic Software: Real-time Vehicle Control Operating System (OS in Narrow Sense)

1.3 Basic Software: Intelligent Driving Middleware (ROS, CyberRT, DDS, AutoSAR)

1.4 Basic Software: How to Systematically Build a Generalized OS for Autonomous Driving?

1.5 Construction of Universal Algorithms for Intelligent Driving: from Small Models to Large Models

1.6 Intelligent Driving General Algorithm Architecture: AI Deep Learning Software Platform

1.7 Intelligent Driving General Algorithm Construction: Intelligent Driving Data Training Set

1.8 Construction of Intelligent Driving General Algorithm: Autonomous Driving System Integration and Engineering Strategy

1.9 Intelligent Driving Terminal-cloud Integration: Data Closed-loop

1.10 Intelligent Driving Terminal-Cloud Integration: Data Collection & Annotation

1.11 Intelligent Driving Terminal-Cloud Integration: Simulation Testing: Scenario Library

1.12 Intelligent Driving Terminal-Cloud Integration: Simulation Testing: Simulation Platform

1.13 Intelligent Driving Terminal-Cloud Integration: Cloud Native and Storage Platform

1.14 Intelligent Driving Terminal-Cloud Integration: HD Map

1.15 Intelligent Driving Assistance Software: ADAS Performance Evaluation

1.16 Intelligent Driving Assistance Software: ADAS Data Recording

2 How to Build Intelligent Cockpit Software System?
2.1 Overall Software and Hardware Architecture of Intelligent Cockpit

2.2 Basic Software: Automotive Non-RTOS (in Narrow Sense)

2.3 Basic Software: Intelligent Cockpit Operating System (in Broad Sense)

2.4 Basic Software: Hypervisor

2.5 Application Algorithm: Application of GPT Model in Intelligent Cockpit

2.6 Application Algorithm: UI Design Software

2.7 Application Algorithm: Voice Software

2.8 Application Algorithm: Acoustics Software

Companies Featured

• CETC iSOFT Infrastructure Software
• ZTE
• RT Thread
• Banma Zhixing
• ZLingsmart
• Kernelsoft Photon
• Aptiv
• QNX
• Xpeng
• Tesla
• LI Auto
• Chang’an
• Toyota
• Geely
• ZEEKR
• Great Wall
• SAIC Z-ONE
• Greenstone
• Baidu
• Bosch
• HoloMatric
• Technomous
• Photo
• Neusoft Reach
• Momenta
• iMotion
• Black Sesame Technologies
• PhiGent Robotics
• ETAS
• DeepRoute
• MAXIEYE
• JueFX
• Huawei
• Thundersoft
• Megatronix
• ECARX
• E Planet
• UAES
• NXP
• Dassault Systemes
• Luxoft
• LinearX
• Kernelsoft
• HiRain

For more information about this report visit https://www.researchandmarkets.com/r/9mctbn

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