Global and China Software-defined Vehicle Industry Research, Panorama and Strategy Report 2023-2024: How to Build Intelligent Driving Software-defined Vehicle (SDV) Architecture?

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Mar 14, 2024, 21:15 ET

DUBLIN, March 14, 2024 /PRNewswire/ -- The "Software-defined Vehicle Research Report, 2023-2024 - Industry Panorama and Strategy" report has been added to ResearchAndMarkets.com's offering.

How to build intelligent driving software-defined vehicle (SDV) architecture?

The autonomous driving intelligent platform can be roughly divided into four parts from the bottom up: hardware platform, system software (hardware abstraction layer + OS kernel + middleware), functional software (library components + middleware), and application algorithm software (autonomous driving, HMI, etc.).

Autonomous driving R&D links mainly involve software engineering and hardware engineering:

Basic software for intelligent driving: real-time vehicle control operating system (narrowly defined OS), intelligent driving middleware (ROS, CyberRT, DDS, AutoSAR), autonomous driving operating system (broadly defined OS), etc.;
General algorithm design for intelligent driving: positioning, perception, planning, decision, etc., covering from small models to foundation models (BEV Transformer, Occupancy Network, autonomous driving end-to-end neural network, etc.);
General algorithm training for intelligent driving: AI deep learning software platform, intelligent driving data training set, etc.;
Terminal-cloud integration for intelligent driving: data closed loop, data collection and labeling, simulation test (scene library, simulation platform), cloud native platform, HD map, etc.;
Intelligent driving system integration and engineering implementation: FCW, LDW, ALC, APA/AVP, etc.
Intelligent driving assistance software: ADAS performance evaluation, ADAS data recording, etc.
Intelligent driving hardware engineering: domain controllers (chips, hardware engineering), sensors (LiDAR, radar, ultrasonic radar, camera, GNSS, IMU, etc.), system engineering, chassis-by-wire, brake-by-wire, etc.;
Intelligent driving hardware system design: computing platform hardware system architecture design, vehicle chip system design, vehicle sensor system design, etc.

As for automakers, emerging carmakers with strong R&D capabilities will be more inclined to build a fully independent intelligent driving "underlying kernel + chip" system:

Tesla: Tesla has created its own RTOS (RT Linux, written in C language) based on the Linux system. On this basis, Tesla has built domain controllers, reconstructed automotive EEA, and applied self-developed FSD SoC;
Li Auto: deeply customized on Linux kernel, Li OS will be first installed on Li Auto's all-electric models. It will also pack Li Auto's self-developed intelligent driving SoC in the future;
NIO: SkyOS, a vehicle all-domain operating system based on Linux kernel, is the underlying operating system for NIO cars. It is installed on NT3.0 platform-based models (e.g., ET9) and is adapted to the chip platforms of NVIDIA, Qualcomm, Intel and others. In addition, it will also be equipped with Shenji NX9031, NIO's self-developed intelligent driving SoC.

Chinese operating system providers have launched open source plans.

Currently, China is quickening its pace of developing open-source vehicle OS:

In 2021 Huawei HarmonyOS was fully donated to the OpenAtom Foundationton to build the OpenHarmony open source project.
In 2022, Banma Zhixing announced that AliOS Drive will effectively enable layered decoupling, cross-domain sharing and open cooperation.

How to build intelligent cockpit architecture for software-defined vehicles (SDV)?

Intelligent cockpit R&D links mainly involve software engineering and hardware engineering:

Cockpit basic software: vehicle operating system (QNX, Linux, Android, HarmonyOS, AliOS, etc.), virtual machine (Hypervisor), middleware (AutoSAR);
Cockpit system software development: application development is mainly based on Android, cluster software development based on QNX, and TBOX software development based on Linux;
Cockpit interface design: UI design software;
Cockpit application software: user portrait, situational awareness, multimodal fusion interaction (AR HUD, voice, acoustics/audio, DMS/OMS, face recognition, gesture recognition and other software development). Foundation models have begun to be used in cockpit multimodal interaction;
Cloud services: vehicle-cloud integrated platform, cloud native platform, information security, OTA development and operation strategy, etc.

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 Mentioned

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/e8yr07

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