Software-defined vehicle Research Report 2022- Architecture Trends and Industry Panorama
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Software-defined vehicle research: 40 arenas, hundreds of suppliers, and rapidly-improved software autonomy

The overall architecture of software-defined vehicles can be divided into four layers:
(1) The hardware platform, heterogeneous distributed hardware architecture;
(2) The system software layer, including hypervisor, system kernels, POSIX, AUTOSAR, etc.;
(3) The application middleware and development framework, including functional software, SOA, etc.;
(4) The application software layer, including smart cockpit HMI, ADAS/AD algorithms, connectivity algorithms, cloud platforms, etc.

In a broad sense, an operating system refers to the middleware based on kernel OS, including system software layer (kernel, hypervisor, middleware), functional software layer (common functional modules and related middleware) and APIs. In a narrow sense, an operating system mainly means the vehicle control OS and automotive OS at the bottom of the system kernel.

SDV 1.png

The operating system in the narrow sense is the cornerstone of building intelligent connected vehicles, while the operating system in the broad sense is the bridge for application development

Microkernel, only to achieve basic task management, memory management, process communication, etc., and other drivers are defined in the user side to achieve, the current commonly used intelligent driving operating systems are mainly Linux, QNX and Other RTOS (such as free RTOS, VxWorks, etc.). The open-source microkernel seL4 based on the third-generation microkernel technology has attracted more and more attention from domestic automakers and technology companies. Li Auto, NIO, Lotus Cars, Horizon Robotics, Xiaomi and other technology companies have joined the seL4 Foundation to promote the development of seL4 microkernel.

The development of smart cars still faces the dilemma - "lack of chips" and "absence of automotive operating systems". The former has received enough attention, while the latter still needs to be broken through. At present, foreign vendors QNX (Blackberry), Linux (open source), and Android (Google) are the core players of the narrowly defined automotive operating systems. For example, in terms of smart cockpit systems, the combination of QNX+Android is the mainstream solution chosen by domestic vendors. China-based Huawei HarmonyOS and Powered by AliOS have made certain breakthroughs, but their self-sufficiency rates are still low. In the field of intelligent driving systems, QNX occupies an absolute monopoly position.

Under the background of autonomous controllability, real-time automotive operating systems signalize an important development direction for filling the gap in automotive operating systems. Many domestic technology companies, including Huawei, ZTE, Baidu Apollo and Banma SmartDrive, are seeking breakthroughs in the field of real-time microkernel operating systems.

SDV 2_副本.png

SDV 2-1_副本.png

In a broad sense, an operating system is a bridge for developers to facilitate the development of application algorithms, and it is a development platform including an operating system in the narrow sense, and middleware, etc.

We divide operating systems in a broad sense into several categories:

  • General operating system for autonomous driving
  • General operating system for smart cockpit
  • General operating system for vehicle-cloud integration

Typical general operating systems for autonomous driving include the AUTOSAR CP and AP integrated solutions from iSoft Infrastructure Software, Baidu Apollo AI Open Source Platform, Autoware ROS2.0 Open Source Platform, Huawei AOS, TTTech MotionWise, EMOS from Enjoy Move Technology, ICVOS from AICC, ZF Middleware and so on.

Based on the mass production of AUTOSAR CP, iSoft Infrastructure Software provides AUTOSAR CP+AP integrated solutions for security domain and high-performance computing domain. With its cloud system, it attains intelligent connectivity. The integrated solutions of iSoft Infrastructure Software can be applied to intelligent cockpit domain, vehicle control system domain and ADAS/AD domain. By standardizing the interfaces and architectures of different operating systems, underlying hardware and protocol software, it forges service-oriented software architectures. As for intelligent cockpit domain and ADAS/AD domain, iSoft Infrastructure Software is developing the corresponding operating system kernels to fully lay out automotive basic software platforms.

In addition, autonomous driving SoC chip vendors are not satisfied with just providing hardware, but also seeking a share in the autonomous driving ecosystem to increase barriers to entry. Nvidia has launched DriveWorks open source platform for autonomous driving, and Horizon Robotics has unveiled - TogetherOS?, a real-time automotive operating system with a secure microkernel architecture.

Not to be outdone, OEMs are considering developing their own autonomous driving operating systems, especially the first batch of emerging automakers utilize AUTOSAR Classic Platform +DDS to build autonomous driving operating systems (development platforms). As the technology ecology continues to mature, emerging automakers and OEMs in transition are making efforts to develop autonomous driving operating systems by themselves.

20120114.gifTesla.OS (Version) is developed by Tesla itself based on underlying Linux. In terms of functional software, it supports PyTorch, a deep learning programming framework.
20120114.gifVW.OS, based on Linux+AUTOSAR Adaptive, features decoupling of software and I/O functions as well as SOA.
20120114.gifToyota’s Woven Planet Group is integrating Apex.OS SDK into its own vehicle development platform, called Arene. The Apex SDK will handle  safety-critical applications and aims to speed up autonomous software development and ultimately bring it to production vehicles.
20120114.gifLi Auto is developing its own Li OS and plans to create a cross-domain intelligent operating system platform. Li OS targets autonomous driving, and will be connected with intelligent vehicle control and intelligent cockpits in the future.

SDV 3_副本.png

In terms of automotive middleware (AUTOSAR, ROS2, Cyber RT), different autonomous driving operating system vendors have different options. For example, Baidu Apollo uses the self-developed CyberRT, Autoware adopts ROS2, and other vendors welcome AUTOSAR Classic and AUTOSAR Adaptive. In recent years, Apex.AI OS (compatible with ROS 2 API) has been widely supported by some European OEMs and Tier1 suppliers. Apex.AI has been invested by many leading enterprises in the automotive industry, such as Continental, Toyota, ZF, Jaguar Land Rover, Volvo, Hella and Daimler Truck.

In the field of autonomous driving, the functions of middleware involve communication, module upgrade, task scheduling and actuation management, but its main function lies in communication. The introduction of communication middleware (DDS, SOME/IP, MQTT) can help developers improve efficiency. At present, communication middleware mainly includes SOME/IP, DDS and MQTT. At present, SOME/IP and DDS are two kinds of communication middleware that are most used in autonomous driving.

SOME/IP communication middleware
SOME/IP middleware providers include AUTOSAR toolchain vendors, like foreign companies such as Vector, ETAS, EB, etc., and domestic companies represented by iSoft Infrastructure Software, Jingwei Hirain Technologies, etc. The GENIVI Alliance provides an open source version of SOME/IP.

DDS communication middleware
The commercial closed-source communication middleware is mainly represented by RTI Connext DDS, which accounts for more than 80% of market share. Xpeng is the first enterprise in China that applies Connext DDS to autonomous vehicles. HoloSAR, the autonomous driving middleware of HoloMatic Technology, also integrates RTI Connext DDS.

Other open source communication middleware includes OPEN DDS, FAST DDS, Cyclone DDS, etc. In recent years, a number of communication middleware products have emerged, including iceoryx from Bosch ETAS, Swift from Greenstone, and MotionWise Cyclone DDS. In addition, the new version of AUTOSAR Adaptive bolster DDS in terms of communication management, and the AP products developed by iSoft Infrastructure Software endorse the integration of third-party DDS.

MQTT communication middleware
It can be used in low-bandwidth and unreliable network scenarios to provide data transmission and monitoring of remote devices based on cloud platforms with the IoT communication protocol MQTT.

Autonomous driving toolchain software is becoming an arena

An autonomous driving system consists of perception, planning and decision-making. The key to algorithm research and development mainly lies in data collection, perception model training, simulation tests and real vehicle tests, etc. Perception and planning constitute the main part of data generation, including data collection, data cleaning and data annotation.

Through a series of toolchains, a complete full-chain data closed-loop development process for autonomous driving is formed, featuring a fully closed loop and self-growth. This poses an important technical barrier for major OEMs and Tier1 suppliers in developing autonomous driving systems. The software and data services involved include:

AI deep neural network learning software
The software/framework involved mainly includes TensorFlow/ PyTorch/ OpenCV/ TensorRT, etc. In China, Baidu has launched Paddle Paddle and Huawei has released MindSpore.

20120114.gif Transformer neural network models can better realize modeling in the space-time dimension, and has been applied in production autonomous vehicles on a large scale:
Transformer is one of the core modules in Tesla’s FSD system algorithm. After image features are extracted, the combination of AI neural network algorithms such as Transformer, CNN and 3D convolution completes cross-time image fusion, so as to output 3D information based on 2D image formation.
20120114.gifDomestically, Haomo.AI has proposed to effectively integrate Transformer with massive data. The MANA data intelligence system of Haomo.AI adopts Transformer to fuse vision and LiDAR data at the underlying layer, and then accomplishes deep perception of space, time and sensors.
20120114.gifXpeng G9 has deployed Transformer: after continuous optimization, the 122% Orin-X computing power required by the dynamic XNet has been reduced to 9% now.

Autonomous driving data collection and automatic annotation system

According to IDC, by 2025, the market size of China's artificial intelligence data collection and annotation services will hit RMB12.34 billion, mainly driven by the data collection and annotation of autonomous vehicles. Thus, there is demand for data collection, processing, storage, training software and tools.

Xnet, Xpeng's "next-generation perception architecture", can generate a "HD map" in real time when it is combined with all sensors in the vehicle. Through the dynamic XNet, the speed and intention of dynamic objects can be recognized more accurately. XNet requires massive data collection, annotation, training and deployment. Xpeng has independently developed an automatic annotation system.

However, many other automakers may cooperate with partners in data collection and annotation. Typical vendors include Speechocean (a global AI training data service provider), Huawei Octopus (data collection, training and simulation services), Vector (CANape, a data collection tool), Appen China (AI data collection and annotation services), ExceedData (data collection and annotation platforms), etc.

Autonomous driving training data set
For autonomous driving with deep learning as the main method, training data sets are the most critical. Algorithms are similar (in particular, many of them are open source), so it is impossible to tell which is the best. Deep learning data sets are related to the final results, so that the former plays a decisive role. The wider the coverage of training data sets, the finer the annotation, the more accurate the classification, the more types, the better the final autonomous driving performance.

Many self-driving companies, including Argoverse of Volkswagen-Ford joint venture Argo, Waymo's Open, Baidu's ApolloScape, Nvidia (PilotNet), Honda (H3D), Aptiv(nuScense) have all disclosed some of their training validation datasets, some provides open-source download link. Now the most influential ones are KITTI, Waymo Open and Aptiv nuScenes. 

There are few datasets with local characteristics in China, mainly including Huawei "ONCE", the vehicle-road collaborative autonomous driving dataset "DAIR-V2X", Jinqiao "JICD" dataset, the large-scale driving behavior dataset DBNet jointly released by Xiamen University and Shanghai Jiaotong University, Xi'an Jiaotong University and Chang'an University jointly constructed and disclosed DADA dataset.

SDV 4_副本.png

Autonomous driving data storage and computing center (cloud services)

Data storage and management only embody the basic capabilities of cloud services. The demand of automakers for cloud services has shifted from IaaS and PaaS to SaaS (Software as a Service). Cloud service providers are expected to provide or integrate a unified toolchain, open up upstream and downstream links, and help automakers quickly go through the data closed-loop chain.

Xpeng and Alibaba Cloud have jointly built Fuyao, the largest intelligent computing center for autonomous driving in China, which shortens the time for single-machine full-precision training from 276 days to 32 days. If 80 machines are running simultaneously, it only takes 11 hours, with the processing speed accelerated by 602 times.

SDV 5_副本.png

Large-scale simulation testing and data training

A simulation system includes a simulation scenario library, a simulation test platform and simulation evaluation, which complement each other.

Here, we take the autonomous driving simulation scenario library as an example. In September 2022, Deqing County, Huzhou City teamed up with Alibaba Cloud and Haomo.AI to release "China's first large-scale autonomous driving scenario library based on CVIS", which uses real traffic data and meets data compliance requirements. It will further accelerate the maturity of autonomous driving in China and the coordinative development of vehicles, roads and cloud. In addition, CATARC, CAERI, Tencent (TAD Sim), Baidu Apollo, etc. offer autonomous driving scenario libraries.

1 Vehicle Basic Software
1.1 Automotive Operating System in Narrow Sense
1.1.1 Intelligent Vehicle software architecture includes hypervisor, system cores, middleware, functional software, and application programs 
1.1.2 Intelligent Vehicle Software Ecological Framework
1.1.3 Automotive Operating System Working Process: Kernel is the core of automotive software architecture 
1.1.4 Automotive Operating System Classification
1.1.5 Classification: Real-time and Non-real-time Operating Systems
1.1.6 Automotive Operating System Kernels are Divided into Three Types: Micro Kernel, Macro Kernel, and Hybrid Kernel
1.1.7 Vehicle Control Operating System and In-vehicle Operating System (1)
1.1.8 Vehicle Control Operating System and In-vehicle Operating System(2)
1.1.9 Vehicle Control Operating System and In-vehicle Operating System (3)
1.1.9 Vehicle Control Operating System and In-vehicle Operating System (3)
1.1.10 Automotive Operating System Market Size 
1.1.11 RTOS (in narrow sense) Suppliers and Product List (1)
1.1.12 RTOS (in narrow sense) Suppliers and Product List(2)
1.1.13 RTOS (in narrow sense) Suppliers and Product List (3)
1.1.14 RTOS (in narrow sense) Suppliers and Product List (4)
1.1.15 Non- RTOS (in narrow sense) Suppliers and Product List (1)
1.1.16 Non- RTOS (in narrow sense) Suppliers and Product List (2)
1.1.17 Comparison of Automotive Operating System in narrow sense: QNX/ Linux / Other RTOS
1.1.18 WindRiver VxWorks Microkernel Architecture (1)
1.1.19 WindRiver VxWorks Microkernel Architecture (2)
1.1.20 Green Hills Software Integrity RTOS 
1.1.21 ZTE GoldenOS Solution(1):Micro kernel and Macro kernel Technology Architecture 
1.1.22 ZTE GoldenOS Solution(2): Intelligent Driving OS Solution 
1.1.23 ZTE GoldenOS Solution(3): Intelligent Cockpit OS Solution

1.2 Autonomous Driving OS in Broad Sense 
1.2.1 Definition
1.2.2 Evolution Trend 
1.2.3 Market Size of China Autonomous Driving Operating System in Broad Sense(Middleware OS)

1.2.4 Autonomous Driving OS Layout of Suppliers Main Autonomous Driving OS Suppliers and Product List (1) Main Autonomous Driving OS Suppliers and Product List (2) Main Autonomous Driving OS Suppliers and Product List (3) Main Autonomous Driving OS Suppliers and Product List (4) Main Autonomous Driving OS Suppliers and Product List (5) Main Autonomous Driving OS Suppliers and Product List (6) i-Soft Infrastructure Software AUTOSAR CP+AP Integrated Solution UnTouch Technology High-Reliability Autonomous Driving Middleware HoloMatic Autonomous Driving Middleware HoloSAR

1.2.5 Open Source Software Platform Autonomous Driving OS Layout Open Source Software Platform Autonomous Driving OS Suppliers and Product List  (1) Open Source Software Platform Autonomous Driving OS Suppliers and Product List  (2) Open Source Software Platform Autonomous Driving OS Suppliers and Product List  (3) Open Source Autonomous Driving OS: Features of Baidu Apollo Open Source Autonomous Driving OS: Apollo 7.0 Software and Hardware Architecture Open Source Autonomous Driving OS: Function of NVIDIA Drive Open Source Autonomous Driving OS: Horizon Micro kernel Architecture RTOS--Together OS Open Source Autonomous Driving OS: Apex.AI  OS gets Supports from Overseas OEMs Open Source Autonomous Driving OS: Toyota Introduces Apex.OS Open Source Autonomous Driving OS: Apex.AI  Committed to Lowering the high Technical Threshold of Autosar Open Source Autonomous Driving OS: Apex.AI Apex.OS and Apex.Autonomy Open Source Autonomous Driving OS: The core of Apex.Middleware is Cyclone DDS and iceoryx Open Source Autonomous Driving OS: Autoware Open Source Autonomous Driving OS: :Autoware All-in-one“ Open Source Software Architecture

1.2.6 Autonomous Driving OS Layout of OEMs Autonomous Driving OS Layout of OEMs (1) Autonomous Driving OS Layout of OEMs (2) Autonomous Driving OS Layout of OEMs (3) Autonomous Driving OS Layout of OEMs (4) Autonomous Driving OS Layout of OEMs (5) Autonomous Driving OS Layout of OEMs (6) Autonomous Driving OS Layout of OEMs (7) BYD OS SAIC Z-ONE SOA Integrated Software Platform LiOS(Li auto OS)

1.3 Intelligent Cockpit General  Operating System (OS)
1.3.1 Intelligent Cockpit Operating System (Software Platform): System Framework
1.3.2 Intelligent Cockpit Operating System (Software Platform): Underlying Core OS 
1.3.3 Intelligent Cockpit Operating System (Software Platform): Secondary Development Based on the Underlying OS
1.3.4 Market Share of Cockpit Operating Systems for New Vehicles 
1.3.5 In-vehicle OS Will Evolve from Cockpit OS to Vehicle OS
1.3.6 Market Prospect of Intelligent Cockpit General Operating System
1.3.7 List of Intelligent Cockpit General Operating System Providers and Products (1)
1.3.8 List of Intelligent Cockpit General Operating System Providers and Products (2)
1.3.9 List of Intelligent Cockpit General Operating System Providers and Products (3)
1.3.10 List of Intelligent Cockpit General Operating System Providers and Products (4)
1.3.11 List of Intelligent Cockpit General Operating System Providers and Products (5)
1.3.12 Thundersoft Cockpit Middleware OS Software Architecture
1.3.13 Banma Zhixing AliOS Intelligent Cockpit Operating System
1.3.14 Huawei Harmony Cockpit Software Platform - HOS-A
1.3.15 Megatronix Cockpit Solution - SmartMega? OS+ (Distributed)
1.3.16 ECARX Cross-platform General Operating System Software Framework - EAS Core
1.3.17 SAIC Z-ONE SOA Software Platform
1.3.18 Geely ECARX Galaxy OS
1.3.19 Great Wall Self-developed Cockpit Operating System - GC-OS
1.3.20 VolvoCars.OS

1.4 Vehicle-cloud Integrated General Operating System (SOA Platform)
1.4.1 Background of Demand for Vehicle-cloud Integrated Architecture
1.4.2 Purpose of Deploying Vehicle-cloud Integrated SOA Software
1.4.3 Core Architecture of Vehicle Intelligent Computing Basic Platform SOA 
1.4.4 Adaptability of SOA to Next-generation Autonomous Driving
1.4.5  Release of White Paper on SOA Software Architecture of Vehicle Intelligent Computing Basic Platform
1.4.6 List of Vehicle-cloud Integrated OS Providers and Products (1)
1.4.7 List of Vehicle-cloud Integrated OS Providers and Products (2)
1.4.8 List of Vehicle-cloud Integrated OS Providers and Products (3)
1.4.9 List of Vehicle-cloud Integrated OS Providers and Products (4)
1.4.10 List of Vehicle-cloud Integrated OS Providers and Products (5)
1.4.11 Vehicle-Cloud Integrated SOA Platform Layout of OEMs (1)
1.4.12 Vehicle-Cloud Integrated SOA Platform Layout of OEMs (2)
1.4.13 iSoft Vehicle-cloud Integrated Solutions
1.4.14 Z-ONE Galaxy Full Stack 4+1: "Hardware, Software, Computing, and Data"
1.4.15 Z-ONE Galaxy Full Stack SOA Software Architecture 3.0
1.4.16 Z-ONE Cloud-pipe-terminal Integrated SOA Software Platform Landed
1.4.17 SOA Software Architecture of Wind River Systems 

1.5 Communication Middleware (DDS, SOME/IP, MQTT)
1.5.1 Communication Middleware: Architecture Definition
1.5.2 Communication Middleware: SOA
1.5.3 Why Do Smart Cars Need Communication Middleware?
1.5.4 Common Types of Communication Middleware (1)
1.5.5 Common Types of Communication Middleware (2)
1.5.6 Standards for Measuring the Quality of Communication Middleware
1.5.7 Development Prospects of SOME/IP & DDS
1.5.8 Mainstream Communication Middleware Suppliers and Their Product Lists (1)
1.5.9 Mainstream Communication Middleware Suppliers and Their Product Lists (1)
1.5.10 Opportunities for local communication middleware vendors
1.5.11 Architecture Diagram of Greenstone "Swift" Communication Middleware (DDS)
1.5.12 Software Framework of RTI Connext DDS
1.5.13 "Zero-copy" and "Shared Memory" Approach of Bosch ETAS "Iceoryx" 

1.6 Automotive Middleware (AUTOSAR, ROS2, CyberRT)
1.6.1 Classification of Automotive Middleware: Autosar, ROS2, CyberRT 
1.6.2 Middleware Solution Options for OEMs and Tier1 Suppliers (1)
1.6.3 Middleware Solution Options for OEMs and Tier1 Suppliers (2)
1.6.4 Development History and Main Features of ROS 2 (1)
1.6.5 Development History and Main Features of ROS 2 (2)
1.6.6 ROS 2 Can Be Used as Autonomous Driving Middleware, Similar to the Middleware Function of AUTOSAR AP
1.6.7 Baidu Apollo Cyber RT: Designed for Autonomous Driving
1.6.8 AUTOSAR AP and Ethernet Communication (SOME/IP) Protocol
1.6.9 Technological Revolution Route of AUTOSAR AP 
1.6.10 System Architecture of AUTOSAR CP
1.6.11 Hybrid Software Architecture based on AUTOSAR CP+AP (1)
1.6.12 Hybrid Software Architecture Based on AUTOSAR CP+AP (2)
1.6.13 Major Global AUTOSAR Suppliers and Their Product Lists (1)
1.6.14 Major Global AUTOSAR Suppliers and Their Product Lists (2)
1.6.15 Major Global AUTOSAR Suppliers and Their Product Lists (3)
1.6.16 Major Chinese AUTOSAR Suppliers and Their Product Lists (1)   
1.6.17 Major Chinese AUTOSAR Suppliers and Their Product Lists (2)  
1.6.18 Major Chinese AUTOSAR Suppliers and Their Product Lists (3)  
1.6.19 AUTOSAR CP and Toolchain of iSoft Infrastructure Software
1.6.20 AUTOSAR AP and Toolchain of iSoft Infrastructure Software
1.6.21 AUTOSAR CP+AP Integrated Solutions of iSoft Infrastructure Software
1.6.22 Automotive Basic Software Ecosystem of iSoft Infrastructure Software
1.6.23 AUTOSAR AP of Shanghai HingeTech Is Applied to TSN Ethernet Multi-domain Controllers

1.7 Hypervisor
1.7.1 Status Quo of Hypervisor Industry 
1.7.2 Application of Smart Cockpit Hypervisors in China
1.7.3 Prospects of Global Automotive Hypervisor Market
1.7.4 Global Hypervisor Suppliers and Their Product Lists (1) 
1.7.5 Global Hypervisor Suppliers and Their Product Lists (2) 
1.7.6 Global Hypervisor Suppliers and Their Product Lists (3) 
1.7.7 Global Hypervisor Suppliers and Their Product Lists (4) 
1.7.8 Global Hypervisor Suppliers and Their Product Lists (5) 
1.7.9 Global Hypervisor Suppliers and Their Product Lists (6) 
1.7.10 Chinese Hypervisor Suppliers and Their Product Lists
1.7.11 ZlingSmart RAITE Hypervisor: System Design
1.7.12 ZlingSmart RAITE Hypervisor: Intelligent Cockpit Solution 

2 Vehicle Tool Software
2.1 Automotive Electronics Software Development Tool Chain
2.1.1 Considerations for Automotive Architecture Design
2.1.2 V-model Software Development Process and Development Tools
2.1.3 Common Automotive Development Tools and Tool Chains in the Industry
2.1.4 Suppliers and Products of E/E Architecture and Platform Development Tool (1)
2.1.5 Suppliers and Products of E/E Architecture and Platform Development Tool (2)
2.1.6 Suppliers and Products of E/E Architecture and Platform Development Tool (3)
2.1.7 Suppliers and Products of E/E Architecture and Platform Development Tool (4)
2.1.8 Suppliers and Products of E/E Architecture and Platform Development Tool (5)
2.1.9 Suppliers and Products of E/E Architecture and Platform Development Tool (6)
2.1.10 Suppliers and Products of E/E Architecture and Platform Development Tool (7)
2.1.11 Suppliers and Products of E/E Architecture and Platform Development Tool (8)
2.1.12 ETAS ES830 Rapid Prototyping Module Software and Hardware Tool Chain
2.1.13 ETAS Embedded Software Development Platform Based on ASCET
2.1.14 dSPACE Autonomous Driving Development Process
2.1.15 Wind River Continuous Integration/Continuous Delivery (CI/CD) Development Model
2.1.16 Industry First Cloud-Native Platform: Wind River Studio
2.1.17 NI LabVIEW Connects with Baidu Paddle Paddle Toolchain
2.1.18 Luxoft "E/E System and SOA Software" Solution
2.1.19 Luxoft "Intelligent Driving R&D Platform" Solution

2.2 AI Deep Learning Software
2.2.1 Suppliers and Products of AI Deep Learning Software (1)
2.2.2 Suppliers and Products of AI Deep Learning Software (2)
2.2.3 Huawei AI Computing Framework MindSpore
2.2.4 Major Domestic and Foreign AI Models
2.2.5 Transformer for Large-Scale Application in Mass Production Autonomous Vehicles
2.2.6 Tesla Transformer Neural Network Enables Multi-Camera Data Fusion
2.2.7 Tesla FSD Deep Learning Code Ratio Increases
2.2.8 HAOMO.AI MANA System Adopts Transformer Neural Network
2.2.9 Deployment of the XPeng G9 Transformer Network

2.3 Data Training Dataset 
2.3.1 Why to Build Dataset? (1)
2.3.2 Why to Build Dataset? (2)
2.3.3 How to Collect Data? 
2.3.4 Dataset Development Trend: Evolve from Single Vehicle Intelligence to Vehicle-City Fusion 
2.3.5 Huawei “ONCE” Training Dataset
2.3.6 The First CVIS Autonomous Driving Dataset “DAIR-V2X”
2.3.7 Waymo "Open" Data Training Dataset 
2.3.8 Argo“Argoverse”Data Training Dataset
2.3.9 Autonomous Driving Dataset Product Comparison (1)
2.3.10 Autonomous Driving Dataset Product Comparison(2)
2.3.11 Autonomous Driving Dataset Product Comparison(3)
2.3.12 Autonomous Driving Dataset Product Comparison(4)
2.3.13 Data Training Dataset Suppliers and Product List (1)
2.3.14 Data Training Dataset Suppliers and Product List (2)
2.3.15 Data Training Dataset Suppliers and Product List (3)
2.3.16 Data Training Dataset Suppliers and Product List (4)
2.3.17 Data Training Dataset Suppliers and Product List (5)

2.4 Data Collection &Annotation Tool Software 
2.4.1 Autonomous Driving Data Collection & Annotation Market Development Trend 
2.4.2 Data Collection & Annotation Platform: Architecture Design 
2.4.3 Data Collection & Annotation Platform: Difficulties of Data Collection 
2.4.4 Data Collection & Annotation Platform: Data Collection Process and Method 
2.4.5 Data Collection & Annotation Platform: Data Collection & Annotation Process
2.4.6 Data Collection & Annotation Platform: Backend Simulation of Data Collection 
2.4.7  AI Data Annotation Company Ranking in China
2.4.8 Autonomous Driving Data Collection & Annotation Tool Software Suppliers and Product List (1)
2.4.9 Autonomous Driving Data Collection & Annotation Tool Software Suppliers and Product List (2)
2.4.10 Autonomous Driving Data Collection & Annotation Tool Software Suppliers and Product List (3)
2.4.11 Autonomous Driving Data Collection & Annotation Tool Software Suppliers and Product List (4)
2.4.12 Autonomous Driving Data Collection & Annotation Tool Software Suppliers and Product List (5) 
2.4.13 Huawei Octopus Data Automatic Annotation Service 
2.4.14 Haitian Ruisheng Autonomous Driving Data Collection & Annotation Business (1)
2.4.15 Haitian Ruisheng Autonomous Driving Data Collection & Annotation Business (2)
2.4.16 KIIS Intelligent Driving Data One-Click Analysis Software 
2.4.17 Xpeng G9  Launches“XNet”
2.4.18 Xpeng Automatic Annotation System 

2.5 Data Closed Loop Tools
2.5.1 Importance of Closed-loop Data for L3/L4 Autonomous Driving
2.5.2 Autonomous Driving Data Closed-loop Technologies (I): Autonomous Driving Data-driven Models
2.5.3 Autonomous Driving Data Closed-loop Technologies (II): Cloud Computing Platform Infrastructure and Big Data Processing 
2.5.4 List of Autonomous Driving Data Closed-loop Providers and Products (1)
2.5.5 List of Autonomous Driving Data Closed-loop Providers and Products (2)
2.5.6 List of Autonomous Driving Data Closed-loop Providers and Products (3)
2.5.7 Autonomous Driving Data Closed-loop Cases (I): Tesla Autopilot Data Engine Framework
2.5.8 Autonomous Driving Data Closed-loop Cases (II): Waymo Data Closed-loop Platform
2.5.9 Autonomous Driving Data Closed-loop Cases (III): NVIDIA Machine Learning Platform
2.5.10 Autonomous Driving Data Closed-loop Cases (IV): Momenta Flywheel Model
2.5.11 Autonomous Driving Data Closed-loop Cases (V): EXCEEDDATA Flexible Data Acquisition Platform Solution
2.5.12 Autonomous Driving Data Closed-loop Cases (VI): Data Closed Loop Solution of Black Sesame Technologies
2.5.13 Autonomous Driving Data Closed-loop Cases (VII): Data Intelligence System Product - MANA (Snow Lake) (1)
2.5.14 Autonomous Driving Data Closed-loop Cases (VII): Data Intelligence System Product - MANA (Snow Lake) (2)
2.5.15 Autonomous Driving Data Closed-loop Cases (VII): Data Intelligence System Product - MANA (Snow Lake) (3)
2.5.16 Autonomous Driving Data Closed-loop Cases (VII): Data Intelligence System Product - MANA (Snow Lake) (4)
2.5.17 Autonomous Driving Data Closed-loop Cases (VII): Data Intelligence System Product - MANA (Snow Lake) (5)

2.6 Data Desensitization Software SDK
2.6.1 Data Classification Standards in "Technical Requirements for Data Security of Telematics Information Services"
2.6.2 Regulations on Data Desensitization and Cyber Security
2.6.3 Technical Requirements and Methods for Automobile Transmission Video and Image Masking
2.6.4 List of Data Desensitization Tool Software Providers and Products (1)
2.6.5 List of Data Desensitization Tool Software Providers and Products (2)

2.7 Simulation & Testing Software 
2.7.1 Autonomous Driving Simulation and Testing
2.7.2 Autonomous Driving Simulation and Testing: Processor-in-the-Loop Simulation
2.7.3 Global Mainstream Autonomous Driving Simulation Software Companies
2.7.4 Market Prospects of Autonomous Driving Simulation Test Software
2.7.5 Suppliers and Products of Autonomous Driving Simulation Software: Traffic Flow Simulation
2.7.6 Suppliers and Products of Autonomous Driving Simulation Software: Vehicle Simulation (1)
2.7.7 Suppliers and Products of Autonomous Driving Simulation Software: Vehicle Simulation (2)
2.7.8 Suppliers and Products of Autonomous Driving Simulation Software: Vehicle Simulation (3)
2.7.9 Autonomous Driving Simulation Case (1): Macro, Meso and Micro Traffic Flow Planning and Simulation of 51WORLD 
2.7.10 Autonomous Driving Simulation Case (2): JUN Sim Cloud

2.8 Autonomous Driving Typical Scenario Library
2.8.1 Building Process of Autonomous Driving Scenario Library
2.8.2 Suppliers and Standardization Organizations for Autonomous Driving Scenario Library (1)
2.8.3 Suppliers and Standardization Organizations for Autonomous Driving Scenario Library (2)
2.8.4 Autonomous Driving Scenario Library Case (1): Large-scale Autonomous Driving Scenario Library Based on Vehicle-Infrastructure Cooperation Cloud Service
2.8.5 Autonomous Driving Scenario Library Case (2): Apollo Autonomous Driving Scenario Library and DevOps Toolchain

2.9 Chip Development Tool Chain 
2.9.1 Product Layout of Chip Development Tool Chain (1)
2.9.2 Product Layout of Chip Development Tool Chain (2)
2.9.3 Product Layout of Chip Development Tool Chain (3)
2.9.4 “Horizon OpenExplorer Platform” AI Development Platform
2.9.5 Data Closed-loop Development Platform: AIDI
2.9.6 Black Sesame Chip Development Tool: Shanhai AI Tool Platform 
2.9.7 SemiDrive AD Platform: UniDrive
2.9.8 “HUAWEI Octopus” Open AD Platform Architecture (1)
2.9.9 “HUAWEI Octopus” Open AD Platform Architecture (2)
2.9.10 NVIDIA New Generation Autonomous Vehicle Platform: Drive Hyperion 8
2.9.11  NVIDIA AD Full Stack Tool Chain
2.9.12 NVIDIA AD Software Stack:  NVIDIA DRIVE ? OS
2.9.13 NVIDIA AD Software Stack:  NVIDIA DRIVE ? OS
2.9.14 NVIDIA AD Software Stack:  Functions (1)
2.9.15 NVIDIA AD Software Stack:  Functions (2)
2.9.16 NVIDIA AD Software Stack:  Functions (3)
2.9.17 NVIDIA DRIVE AP2X Software Solutions
2.9.18 NVIDIA Autonomous Driving Simulation Platform: Drive Sim Omniverse Replicator
2.9.19 NVIDIA AI Assisted Driving Platform: Drive Chauffeur 
2.9.20 Tesla Dojo Supercomputing Training Platform: Self-developed 7nm AI Training Chip D1

2.10 ADAS Performance Evaluation Software
2.10.1 List of ADAS Power Consumption and Performance Evaluation Software Providers and Products 
2.10.2 Vehicle ADAS Power Consumption Evaluation Software Requirements
2.10.3 Vehicle ADAS Performance Evaluation Tool: ViCANdo Extended Tool Set (ICVT)

2.11 ADAS Data Logging Software
2.11.1 ADAS Data Recording Requirements (Validation Test Link)
2.11.2 ADAS Data Logging Requirements (Post-delivery)
2.11.3 Definition of L3 Autonomous Driving System
2.11.4 What Are The Requirements of L3 System for Data Logging?
2.11.5 List of Data Logging Tool Software Providers and Products (1)
2.11.6 List of Data Logging Tool Software Providers and Products (2)
2.11.7 NI Completes ADAS Verification by Cooperating with Others 
2.11.8 Vector's Solution for ADAS Data Logging System

2.12 Automotive Software Test System Software
2.12.1 Automotive Software Test Evaluation Models and Standards 
2.12.2 Automotive Software Test Steps
2.12.3 List of Automotive software Test Service Providers and Products (1)
2.12.4 List of Automotive software Test Service Providers and Products (2)
2.12.5 List of Automotive software Test Service Providers and Products (3)
2.12.6 List of Automotive software Test Service Providers and Products (4)

3 Application and Algorithm Software - Autonomous Driving
Preface: Overall Software and Hardware Architecture of Autonomous Driving

3.1 Development Trends of Autonomous Driving Algorithms
3.1.1 Classification of Autonomous Driving Algorithms (1)
3.1.2 Classification of Autonomous Driving Algorithms (2)
3.1.3 Types and Development Cycle of ADAS/AD Algorithm Software
3.1.4 Deep Learning Models and Data Algorithms: The Divine Move in Autonomous Driving
3.1.5 Deep Learning Models and Data Algorithms: Data-driven ADAS Development Flow Chart
3.1.6 Perception Algorithms: Pre-Fusion (Tightly Coupled) Combines Multiple Sensors
3.1.7 Perception Algorithms: Post-fusion (Loosely Coupled) Re-fusions Sensor Results
3.1.8 Perception Algorithms: Tesla Perception Algorithm Structure Is Divided Into Four Parts
3.1.9 Decision Algorithms: Need to Rely on Efficient AI Models and Massive Training Data
3.1.10 Decision Algorithms: Driving Situational Cognition
3.1.11 Decision Algorithms: Planning Control Core Objectives and Demands
3.1.12 Various Levels of Autonomous Driving Solutions and Penetration Rates

3.2 Passenger Car Autonomous Driving Algorithm Software
3.2.1 List of L2+ Driving-Parking Integration Algorithm Providers and Products 
3.2.2 List of L3/L4 Driving-Parking Integration Algorithm Providers and Products (1)
3.2.3 List of L3/L4 Driving-Parking Integration Algorithm Providers and Products (2)
3.2.4 List of L3/L4 Driving-Parking Integration Algorithm Providers and Products (3)
3.2.5 List of L3/L4 Driving-Parking Integration Algorithm Providers and Products (4)
3.2.6 SPACE Self-develops SPACE-OS System, Having Launched L3 Parking Products
3.2.7 Valeo L3 and L3+ Autonomous Driving Solutions
3.2.8 Safety Level Distribution of Main ECU and Backup ECU in Valeo L3+ Autonomous Driving
3.2.9 Huawei Full Stack Autonomous Driving Solution (ADS)
3.2.10 AICC iVBB2.0-based Family Products 
3.2.11 AICC iVBB2.0-based "Sensing Computing Separation" Solution
3.2.12 Algorithm Porting and Performance Optimization of QNX in Autonomous Driving Assistance

3.3 Commercial Vehicle Autonomous Driving Algorithm Software
3.3.1 Commercial Vehicle L3/L4 Autonomous Driving System Integrators
3.3.2 List of L3/L4 Commercial Vehicle Autonomous Driving Algorithm Providers and Products (1)
3.3.3 List of L3/L4 Commercial Vehicle Autonomous Driving Algorithm Providers and Products (2)
3.3.4 Multi-sensor Fusion Algorithm and Mass Production Engineering Capabilities of PhiGent Robotics 

3.4 HD Map 
3.4.1 Layers and Update Frequency of HD Maps
3.4.2 HD Map Business Model 
3.4.3 HD Industry Chain 
3.4.4 Passenger Car HD Map Market in China 
3.4.5 Requirements of Autonomous Driving for HD Map Elements: L3/L4 (1)
3.4.6 Requirements of Autonomous Driving for HD Map Elements: L3/L4 (2)
3.4.7 HD Map Suppliers and Product List: Passenger Car  (1)
3.4.8 HD Map Suppliers and Product List: Passenger Car (2)
3.4.9 HD Map Suppliers and Product List: Commercial Vehicle (1)
3.4.10 HD Map Suppliers and Product List: Commercial Vehicle(2)
3.4.11 Application Case 1 of L4 Autonomous Driving HD Map and Positioning: Meituan Autonomous Delivery Vehicle
3.4.12 Application Case 1 of L4 Autonomous Driving HD Map and Positioning: PIX Moving Autonomous Minibus

4 Application and Algorithm Software – Vehicle Control
4.1 Automotive Energy Management Software
4.1.1 Simplified V-shaped Design Flowchart and Automotive Industry Software
4.1.2 Cloud BMS Demand Analysis (1)
4.1.3 Cloud BMS Demand Analysis (2)
4.1.4 Energy Management Software Suppliers and Their Product Lists (1)
4.1.5 Energy Management Software Suppliers and Their Product Lists (2)
4.1.6 Huawei AI BMS Solution
4.1.7 Huawei AI BMS Provides Automakers with Visual Safety Management Platforms for Power Parts  
4.1.8 Four-Step Process of Huawei AI BMS
4.1.9 Cloud-based Networked Battery Architecture of Eatron Technologies

4.2 Vehicle Body and Vehicle Control Software
4.2.1 Functional Features and Software Requirements of Body Domain Control System
4.2.2 Key Technologies and Actual Development of Body Domain Control System
4.2.3 The Integration of Gateways and Body Domain Control Will Become a Trend (1)
4.2.4 The Integration of Gateways and Body Domain Control Will Become a Trend (2)
4.2.5 Potential Market Prospects of Body Domain
4.2.6 Body Domain Control Software (Hardware) Suppliers and Their Product Lists (1)
4.2.7 Body Domain Control Software (Hardware) Suppliers and Their Product Lists (2)
4.2.8 Body Domain Control Software (Hardware) Suppliers and Their Product Lists (3)
4.2.9 Body Domain Control Software (Hardware) Suppliers and Their Product Lists (4)
4.2.10 Body Domain Control Software (Hardware) Suppliers and Their Product Lists (5)
4.2.11 Development Trend of Vehicle Control Domain: Integration and Domain Control (1)
4.2.12 Development Trend of Vehicle Control Domain: Integration and Domain Control (2)
4.2.13 Vehicle control domain Software Platform Solution
4.2.14 FMT VCOS (Vehicle Control Operating System)
4.2.15 FMT’s Deployment in Central Controllers, Power Domain Controllers and Zone Controllers

4.3 Power and Drive Management Software
4.3.1 Common Automotive Simulation Industrial Design Software
4.3.2 Dynamic and Economic Simulation Analysis Software
4.3.3 Dynamic Simulation Analysis Software Suppliers and Their Product Lists (1)
4.3.4 Dynamic Simulation Analysis Software Suppliers and Their Product Lists (2)
4.3.5 Automotive Electronic Control Forward Design Software Suppliers and Their Product Lists

5 Application and Algorithm Software – Telematics and Cybersecurity
5.1 Roadside Operating System
5.1.1 Definition of Roadside Traffic OS
5.1.2 Suppliers and Products of Roadside Traffic OS (1)
5.1.3 Suppliers and Products of Roadside Traffic OS (2)
5.1.4 SmartRoad OS: Roadside Unit Operating System for Vehicle-Infrastructure-Cloud Integrated Cloud Control System
5.1.5 Six Characteristics of SmartRoad OS Technology Architecture
5.1.9 Tencent Traffic OS
5.2 Vehicle-Infrastructure-Cloud Cooperation Service
5.2.1 Vehicle-Infrastructure-Cloud Cooperation Will Become One of the Mainstream Paths to Achieve High-level Autonomous Driving
5.2.2 Method of Vehicle-Infrastructure-Cloud Cooperation to Realize Autonomous Driving
5.2.3 Suppliers and Products of Vehicle-Infrastructure-Cloud Cooperation Solution (1)
5.2.4 Suppliers and Products of Vehicle-Infrastructure-Cloud Cooperation Solution (2)
5.2.5 Suppliers and Products of Vehicle-Infrastructure-Cloud Cooperation Solution (3)

5.3 V2X Technology Platform
5.3.1 Conventional V2X Technology Platform Architecture
5.3.2 Bottleneck of Conventional V2X Technology Platform
5.3.3 Next-generation V2X Technology Platform Development Direction
5.3.4 Next-generation V2X Technology Platform Achieves High-precision & High-quality Data Acquisition
5.3.5 Four Major Products Module of EXCEEDDATA
5.3.6 Data Acquisition Platform Architecture of EXCEEDDATA V2X

5.4 Cloud Service Platform
5.4.1 Application Scenario Prospects of Automotive Cloud Services in Intelligent Connected Vehicles
5.4.2 Building Model of Auto Companies Cloud Services: Self-built Private Cloud, Procurement of Public Cloud (Hybrid Cloud)
5.4.3 Status of Cloud Service Platform Market Development
5.4.4 Cloud Platform Providers and Business Models (1) 
5.4.5 Cloud Platform Providers and Business Models (2) 
5.4.6  Cloud Platform Providers and Business Models (3) 
5.4.7 Cloud Platform Providers and Business Models (4) 

5.5 Remote Intelligent Diagnosis Platform
5.5.1 Automotive Maintenance Equipment Manufacturers Gradually Moving Toward Cloud-based Maintenance Platforms
5.5.2 Development Status of Remote Cloud Diagnosis
5.5.3 Remote Cloud Diagnosis Provider Solutions
5.5.4 EXCEEDDATA "Vehicle Remote Intelligent Diagnosis System Solution"
5.5.5 EXCEEDDATA " Active and Intelligent Diagnosis Solution"
5.5.6 THINKCAR "Intelligent Cloud Diagnosis Products for New Energy Vehicles"

5.6 OTA
5.6.1 OTA Industry Chain 
5.6.2 OTA Operating Business Model 
5.6.3 OTA TO-B Business Model 
5.6.4 Market Prospects of Automotive OTA
5.6.5 OTA-related Suppliers Business Model (1) 
5.6.6 OTA-related Suppliers Business Model (2) 
5.6.7 OTA-related Suppliers Business Model (3) 

5.7 Automotive Cybersecurity
5.7.1 Definition of Automotive Cybersecurity
5.7.2 Market Prospects of Automotive Cyber Security
5.7.3 Suppliers of Automotive Cybersecurity Software
5.7.4 Suppliers and Products of Automotive Encryption Algorithm (1)
5.7.5 Suppliers and Products of Automotive Encryption Algorithm (2)
5.7.6 Suppliers and Products of Cybersecurity Testing Technology 
5.7.7 Garrett Automotive Cybersecurity Solutions

6 Application and Algorithm Software - Cockpit and Entertainment
Preface: Overall Software and Hardware Architecture of Intelligent Cockpit
6.1 Voice Software
6.1.1 Overview of Human Computer Interaction
6.1.2 Overview of In-vehicle Voice Interaction Technology
6.1.3 Global and Chinese In-vehicle Voice Players
6.1.4 Market Prospects of In-vehicle Voice 
6.1.5 List of Vehicle Voice Software Providers and Products (1)
6.1.6 List of Vehicle Voice Software Providers and Products (2)
6.1.7 List of Vehicle Voice Software Providers and Products (3)
6.1.8 List of Vehicle Voice Software Providers and Products (4)

6.2 Acoustic Software
6.2.1 Summary of Acoustic Software Vendors Business Model 
6.2.2  Evolution of Acoustic Software Procurement Model of Automakers 
6.2.3 Exploration of Acoustic Software Business Model 
6.2.4 Acoustic Software Vendors Business Model (1) 
6.2.5 Acoustic Software Vendors Business Model (2) 
6.2.6 Acoustic Software Vendors Business Model (3) 

6.3 DMS/OMS Software 
6.3.1 Overview of DMS 
6.3.1 Overview of DMS 
6.3.2 DMS Software Technology
6.3.3 Market Prospects of DMS Software Technology
6.3.4 DMS Vision Perception Algorithm Suppliers and Business Models (1)  
6.3.5 DMS Vision Perception Algorithm Suppliers and Business Models(2)
6.3.6 DMS Vision Perception Algorithm Suppliers and Business Models(3)

6.4 Face Recognition and Gesture Recognition Software 
6.4.1 Overview of Face Recognition Interaction Technology 
6.4.2 Major Global and Chinese Face Recognition Players
6.4.3 Market Prospects of Vehicle Face Recognition
6.4.4 Overview of Gesture Recognition and Interaction Technology
6.4.5 Major Global and Chinese Gesture Recognition Players

6.5 AR HUD Software
6.5.1 Overview of AR-HUD Products and Technologies 
6.5.2 AR-HUD Optical Waveguide Technology
6.5.3 AR Engine Software Technology
6.5.4 AR-HUD Applied Vehicle Models
6.5.5 AR-HUD Market Opportunities
6.5.6 List of AR-HUD Software Providers and Products (1)
6.5.7 List of AR-HUD Software Providers and Products (2)
6.5.8 List of AR-HUD Software Providers and Products (3)

6.6 UI Design Software
6.6.1 Overview of Vehicle Interface Design
6.6.2 Classification of HMI Design 
6.6.3 Market Prospects of Cockpit HMI UI/UX Design
6.6.4 List of HMI Design Software Providers and Products (1)
6.6.5 List of HMI Design Software Providers and Products (2)
6.6.6 List of HMI Design Software Providers and Products (3)
6.6.7 List of HMI Design Software Providers and Products (4)
6.6.8 Luxoft UXD Cockpit UI/UX Design Solution

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