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:
R&D Links in Autonomous Driving Basic Software for Intelligent Driving
General Algorithm Design for Intelligent Driving
General Algorithm Training for Intelligent Driving
Terminal-Cloud Integration
System Integration and Engineering Implementation
Intelligent Driving Assistance Software
Hardware Engineering
Hardware System Design
Development Paths for Intelligent Driving OS Kernels
Linux-based Path
Microkernel RTOS Path
Safety Linux is emerging as a significant OS in China, with major developments from companies like ZTE and Banma.
Key Players and Projects ZTE
Banma AliOS Drive
Localization and Open Source Efforts
China’s localization rate for vehicle operating systems is about 5-10%. Efforts include:
Automaker Initiatives Tesla
Li Auto
NIO
Intelligent Cockpit Architecture R&D Links
Cockpit Basic Software
System Software Development
Cockpit Interface Design
Cockpit Application Software
Cloud Services
Cloud-Native Platform Developments
Self-Development
Open Source
Comprehensive Digital Transformation
Examples of Cloud-Native Implementations
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
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
For more information about this report visit https://www.researchandmarkets.com/r/9mctbn
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