Global and China Software-Defined Vehicle Industry Report 2022: Autonomous Driving SoC Chip Vendors are Also Seeking a Share in the Autonomous Driving ecosystem
DUBLIN, Jan. 20, 2023 /PRNewswire/ -- The "Global and China Software-Defined Vehicle Research Report 2022- Architecture Trends and Industry Panorama" report has been added to ResearchAndMarkets.com's offering.
Software-defined vehicle research: 40 arenas, hundreds of suppliers, and rapidly-improved software autonomy
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 - TogetherOST, 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.
Tesla.OS (Version) is developed by Tesla itself based on underlying Linux. In terms of functional software, it supports PyTorch, a deep learning programming framework.
VW.OS, based on Linux+AUTOSAR Adaptive, features decoupling of software and I/O functions as well as SOA.
Toyota'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.
Li 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.
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
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.
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.
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.
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.
Key Topics Covered:
1 Vehicle Basic Software
1.1 Automotive Operating System in Narrow Sense
1.2 Autonomous Driving OS in Broad Sense
1.3 Intelligent Cockpit General Operating System (OS)
1.4 Vehicle-cloud Integrated General Operating System (SOA Platform)
1.5 Communication Middleware (DDS, SOME/IP, MQTT)
1.6 Automotive Middleware (AUTOSAR, ROS2, CyberRT)
2 Vehicle Tool Software
2.1 Automotive Electronics Software Development Tool Chain
2.2 AI Deep Learning Software
2.3 Data Training Dataset
2.4 Data Collection &Annotation Tool Software
2.5 Data Closed Loop Tools
2.6 Data Desensitization Software SDK
2.7 Simulation & Testing Software
2.8 Autonomous Driving Typical Scenario Library
2.9 Chip Development Tool Chain
2.10 ADAS Performance Evaluation Software
2.11 ADAS Data Logging Software
2.12 Automotive Software Test System Software
3 Application and Algorithm Software - Autonomous Driving
3.1 Development Trends of Autonomous Driving Algorithms
3.2 Passenger Car Autonomous Driving Algorithm Software
3.3 Commercial Vehicle Autonomous Driving Algorithm Software
3.4 HD Map
4 Application and Algorithm Software - Vehicle Control
4.1 Automotive Energy Management Software
4.2 Vehicle Body and Vehicle Control Software
4.3 Power and Drive Management Software
5 Application and Algorithm Software - Telematics and Cybersecurity
5.1 Roadside Operating System
5.2 Vehicle-Infrastructure-Cloud Cooperation Service
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