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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