Autonomous Driving Simulation Industry Chain Report (Chinese Companies), 2022
  • Dec.2022
  • Hard Copy
  • USD $3,200
  • Pages:140
  • Single User License
    (PDF Unprintable)       
  • USD $3,000
  • Code: FZQ006
  • Enterprise-wide License
    (PDF Printable & Editable)       
  • USD $4,500
  • Hard Copy + Single User License
  • USD $3,400

Simulation Research (Part II): digital twin, cloud computing, and data closed-loop improve simulation test efficiency.

Simulation tests can not only be conducted in extreme working conditions and more complex scenarios and make ADAS/ADS verification more effective, but also reproduce and generalize the real vehicle test data, allow for deeper analysis of the problems in real vehicle tests and make corresponding optimizations, speeding up function development and shortening test cycle. The higher efficiency of autonomous driving simulation tests comes with the adoption of such technologies as digital twin, cloud computing, and data closed-loop.

1. Digital twin technology will help to build more extreme test scenario combinations.

Scenario libraries are the basis of simulation tests, and digital twin technology is a powerful tool for building virtual scene libraries. To ensure the safety and reliability of vehicles, OEMs need to test almost unlimited scenarios. By referring to the real world, digital twin technology can be used to model a 3D elements library quickly and automatically, and build different roads, marking lines, weathers, surroundings and other scenarios to achieve more possible test scene combinations, thus enabling high-precision simulation of sensors, environments, vehicle dynamic models, etc. Especially in the software OTA regression testing, digital twin can also greatly improve the efficiency of simulation testing and verification.

At present, Chinese comprehensive simulation platforms like Baidu, Huawei, Tencent and Alibaba, as well as specialist simulation testing service providers such as IAE, have all used digital twin technology for scene construction.

Huawei Octopus Platform can convert the collected typical road sections into simulation scenes, and combine them with HD maps to realize digital twin of real scenes. It can not only restore more than 95% scenes, but also give great assistance to developers to quickly simulate surrounding vehicles and realize minute-level scene construction. The platform with built-in 200,000 simulation scenes can provide application tools such as simulation, scene library management, scene fragment and evaluation system, as well as high-concurrency instance handling capabilities.

仿真国内 1_副本.png

Tencent's autonomous driving digital twin simulation test platform TAD Sim (upgraded to 2.0) uses real data and gaming technology as dual-engine drive, covers simulation models such as road scene, traffic flow, vehicle sensing and vehicle dynamics, and supports OpenX and OSI international simulation standards. It offers more than 1,000 scene types, and can also generate larger-scale, rich scenes through generalization.

仿真国内 2_副本.png

Founded in 2018, IAE is committed to building the world's largest simulation test scene workshop (massive scene libraries) with high precision, high confidence, high coverage and high freshness, and providing simulation scene data and SaaS (Scenario-as-a-Service). Its "Shuimu Lingjing" Scene Workshop is built according to the related Chinese and foreign intelligent connected vehicle industry standards, real roads and traffic behavior characteristics. With artificial intelligence and digital twin as underlying technologies, and the cross-platform and big data drive as the principle, the platform can be used to develop and build a whole-process and automated tool chain covering scene data collection, processing, analysis and mass production, realize large-scale, high-quality production of simulation scenes, and build a core support system required for large-scale algorithm training, simulation testing and evaluation. At present, IAE has built more than 8,000 groups of actually available simulation scene libraries, covering city-level digital twin, autonomous driving, Chinese and foreign regulations and standards, CIDAS traffic accident recurrence, safety of the intended functionality, and V2X.

仿真国内 3_副本.png

仿真国内 4_副本.png

2. The simulation testing based on cloud high-concurrency operation will further improve iteration efficiency of ADAS/ADS functions.

For advanced function development and intended functionality development, the autonomous driving simulation test platform needs to offer real restoration test scenes, make good use of collected road data to produce simulation scenes, and be capable of large-scale parallel processing on the cloud, so as to answer the needs of autonomous driving for closed-loop testing of perception, decision and control full-stack algorithms. Currently, technology giants, automakers, solution providers, and simulation software companies are working to expedite the construction of virtual simulation cloud platforms.

仿真国内 5_副本.png

Baidu Apollo Simulation Platform is a cloud service built on Baidu Cloud and Azure. It improves the operating efficiency of simulation platforms through the large-scale distributed and dynamic variable speed simulation. Based on the large-scale cloud computing capacity, Apollo has created a virtual operating capability of millions of kilometers per day, and has built a fast iterative closed loop, making it easy for developers to achieve "millions of kilometers per day", greatly improving the development efficiency.

Alibaba Cloud Autonomous Driving Simulation Platform supports flexible, high-concurrency simulation and provides traffic flow simulation that can generate simulation traffic flows that conform to the element features and control methods of Chinese roads. Combined with autonomous driving simulation software, the platform enables game simulation, completing construction and testing of special scenes such as rainy/snowy weather and poor lighting conditions at night within 30 seconds. The Alibaba Cloud Platform favored 20 times faster autonomous driving simulation for Inceptio in 2022.

IAE "Jellyfish" Massive Simulation SaaS Platform can be deployed on private cloud and public cloud in a modular and elastic manner, and supports hypervisor, Docker and other modes. Besides designing and building cloud simulation platforms for customers, the company also builds a 400-node massive simulation SaaS platform based on proprietary cloud, with the virtual simulation test capability of daily effective mileage of more than one million kilometers, providing customers with SaaS-based simulation test services.

3. Building a data closed loop for autonomous driving simulation testing has become a new topic in the industry.

In the trend for "data-driven intelligence", simulation testing has become a key link in the autonomous driving data closed loop. How to build a data rolling iteration model through a range of simulation tests such as software-in-the-loop, hardware-in-the-loop, and vehicle-in-the-loop, and how to enable data-driven algorithm upgrades through corner cases in simulation tests have become new topics in the industry.
In March 2022, Tencent and Automotive Data of China (ADC) signed a cooperation agreement, under which data closed-loop and simulation testing for mass production becomes one of the R&D priorities.
In September 2022, IAE struck a strategic cooperation agreement with the autonomous driving industry data public service platform VDBP under the China Association of Automobile Manufacturers (CAAM). Through the close partnership with the CAAM and the VDBP platform, IAE will expand as many simulation scene data sources as possible, solve the problems of insufficient original data and single sources, and serve more Chinese and foreign OEMs relying on the platform.    
In November 2022, Baidu announced a data closed-loop compliance solution for autonomous driving. Through the proprietary cloud platform, data decryption and data desensitization are carried out for simulation training, which ensures data compliance and confidentiality while implementing simulation testing.
IAE’s X-IN-LOOP simulation test technology system integrates the concepts of technology closed-loop and data closed-loop throughout the entire vehicle development and verification process, and provides complete technical solutions and services from software/hardware-in-the-loop, driver-in-the-loop, advanced vehicle-in-the-loop, and vehicle-environment-traffic-in-the-loop to digital twin scene libraries and massive cloud computing power simulations, enabling the temporal and spatial acceleration of autonomous driving R&D, testing and verification to power the commercialization of autonomous driving.

仿真国内 6_副本.png

In addition, from simulation objects, it can be seen that the trend for autonomous vehicle and V2X integrated simulation is accelerating. In current simulation software, road signs, marking lines, and road facilities act as static environment elements. As vehicle-infrastructure cooperation and Internet of Vehicles technologies advance, infrastructures such as road perception and communication will participate in the interaction of driving behaviors between autonomous vehicles, and the simulation of vehicle behaviors will pose new technical requirements as urban intelligent infrastructures work.

1 Overview of Autonomous Driving Simulation
1.1 Overview of Autonomous Driving Simulation Technology
1.2 Significance of Simulation Testing to Autonomous Driving R&D
1.3 Types of Autonomous Driving Simulation Technology
1.4 Composition of Autonomous Driving Simulation Software
1.5 Overview Diagram of Simulation Software System
1.6 International Organization for Standardization of Autonomous Driving Simulation: ASAM
1.6.1 Association for Standardization of Automation and Measuring Systems (ASAM)
1.6.2 Chinese ASAM Standards: C-ASAM Working Group
1.6.3 ASAM Standard Domains
1.7 Status Quo of Autonomous Driving Simulation Test Standards in China
1.7.1 Autonomous Driving Road Test Standards - National 
1.7.2 Autonomous Driving Road Test Standards – Provincial/Municipal
1.8 Status Quo of the Formulation of International Standards for Autonomous Driving Test Scenarios in Which China Has Participated
1.9 Progress in China’s Autonomous Driving Function Simulation Standards
1.10 Multi-scenario Multi-engine Simulation Test Service Platform Solutions in China
1.11 Partnerships between OEMs and Autonomous Driving Simulation Platforms in China
1.12 Comparison between Autonomous Driving Simulation Platforms in China
1.12.1 Recent Developments in Autonomous Driving Simulation Platforms in China

2 Autonomous Driving Simulation Platforms and Companies
2.1 PanoSim
2.1.1 Profile
2.1.2 Autonomous Driving Simulation Test Platform
2.1.2 Composition and Functions of Products
2.1.2 Products and Features
2.1.3 xPilot Autonomous Driving Simulation Test Platform
2.1.3 Components of xPilot
2.1.3 Core Advantages of xPilot
2.1.3 Application Scenarios of xPilot
2.1.4 PanoDrive Driving Simulator
2.2 51World
2.2.1 Profile
2.2.2 51Sim-One Simulation Platform 
2.2.2 51Sim-One Simulation Platform: Scenarios
2.2.2 51Sim-One Simulation Platform: Perceptual Simulation
2.2.3 51Sim-One Simulation Platform: Regulatory Control Simulation
2.2.3 51Sim-One Simulation Platform: Cloud Simulation
2.2.3 51Sim-One Simulation Platform: XIL 
2.2.4 Dataverse Data Platform
2.2.5 51Sim-One 2.0
2.2.6 Application Case: Shanghai Motor Vehicle Inspection Certification & Tech Innovation Center (SMVIC)
2.2.7 Cooperation Events
2.3 Huawei
2.3.1 Profile
2.3.2 MDC Platform: Cloud Training and Simulation Services
2.3.3 "Octopus" Autonomous Driving Open Platform
2.3.3 "Octopus" Autonomous Driving Open Platform: One-stop Autonomous Driving DevOps Capabilities
2.3.3 "Octopus" Autonomous Driving Open Platform: Digital Twin and Virtual-Real Hybrid Simulation
2.3.4 Octopus Autonomous Driving Cloud Service of Huawei Cloud
2.3.4 Octopus Autonomous Driving Simulation Platform: Simulation Services
2.3.4 Octopus Autonomous Driving Simulation Platform: Cloud + AI + Hardware and Software + Chip Combined Ecosystem
2.4 Baidu
2.4.1 Apollo Simulation Platform
2.4.1 Apollo Simulation Platform: Scene Library
2.4.1 Apollo Simulation Platform: Evaluation Criteria
2.4.1 Apollo Simulation Platform: Version Iteration
2.4.2 AADS 
2.4.3 Cooperation Events
2.5 Tencent
2.5.1 TAD Sim Autonomous Driving Simulation Platform
2.5.2 TAD Sim Autonomous Driving Simulation Platform: Product Features and Core Capabilities
2.5.3 TAD Sim Autonomous Driving Simulation Platform: Scene Restoration and Digital Twin
2.5.4 TAD Sim Autonomous Driving Simulation Platform: Environment Simulation
2.5.5 TAD Sim Autonomous Driving Simulation Platform: Sensor Simulation
2.5.6 TAD Sim 2.0: Combination of Gaming Technology and Simulation
2.5.6 TAD Sim 2.0: Architecture Upgrade
2.5.7 Derivation of Autonomous Driving Simulation: City-level Simulation Platform
2.5.8 Cooperation Events
2.6 Alibaba (DAMO Academy)
2.6.1 Autonomous Driving Layout of Alibaba
2.6.2 Cloud-based Intelligent Simulation Test Platform 
2.6.3 Hybrid Simulation Test Platform
2.7 IAE 
2.7.1 Profile
2.7.2 X-IN-LOOP Simulation Test Technology System
2.7.3 "Shuimu Lingjing" Scene Workshop (Massive Scene Libraries)
2.7.4 Scene Data Production Closed-loop System 
2.7.4 Continuous Simulation Testing Based on the Scene Workshop
2.7.5 "Jellyfish" Cloud Computing Power Massive Simulation SaaS Platform
2.7.5 Customer Cases of "Jellyfish" Cloud Computing Power Massive Simulation SaaS Platform 
2.7.5 ADAS-AD-V2X Hardware-in-the-Loop (HIL) Products and Customer Cases 
2.7.6 Vehicle-in-the-loop Technology System
2.7.7 Advanced Vehicle-in-the-loop (VaHIL) Simulation Laboratory and Customer Cases
2.7.8 Generate Training Dataset Based on the Scene Workshop
2.8.1 Profile
2.8.2 Scene Library and Simulation System Integrated Solution 
2.8.3 Virtual Simulation Scene Library: i-Scenario
2.8.4 Chinese Typical Driving Scene Library: V3.0
2.8.5 Virtual Simulation Scene Generation/Conversion Software and Hardware-in-the-loop (HIL)
2.8.6 Scene Data Processing Cloud Platform: i-STAR
2.8.7 Scene Data Collection Equipment Platform: i-Collector
2.8.8 Autonomous Driving Evaluation Software: i-Evaluator
2.8.9 Cooperation Events
2.9 Saimo Technology
2.9.1 Profile
2.9.2 Intelligent Connected Vehicle Simulation and Testing Platform: Sim Pro 
2.9.3 Autonomous Driving Function Cloud Platform
2.9.4 Key Technologies and Experimental Environment & Facilities
2.9.5 Cooperation
2.10 CICV 
2.10.1 Profile
2.10.2 Virtual Simulation Test Evaluation
2.10.3 China Intelligent and Connected Vehicle Basic Data Service Platform 
2.10.4 Cooperation Events
2.11 SMVIC
2.11.1 Profile
2.11.2 Autonomous Driving Simulation Test Laboratory
2.11.2 Autonomous Driving Simulation Test Laboratory: Service Capabilities
2.11.2 Autonomous Driving Simulation Test Laboratory: Scene Library
2.11.2 Autonomous Driving Simulation Test Laboratory: Application of Trunk Autonomous Driving 
2.11.3 Sensor Simulation
2.11.4 Automotive Intelligent Computing System Public Service Platform 
2.12 CATARC 
2.12.1 Profile
2.12.2 Driving Scenario Simulation Platform
2.12.3 Autonomous Driving Simulation Cloud Platform: AD Chauffeur 2.0
2.12.3 AD Chauffeur 2.0: Core Features
2.12.3 AD Chauffeur 2.0: Function Upgrades
2.12.4 Scenario Generalization Tool: AD Scenario

3 Autonomous Driving Simulation Trends
3.1 Trend 1
3.2 Trend 2
3.3 Trend 3
3.4 Trend 4
3.5 Trend 5
3.6 Trend 6

China Automotive Digital Key Research Report, 2023

Automotive Digital Key Research: the pace of mobile phones replacing physical keys quickens amid the booming market "China Automotive Digital Key Research Report, 2023" released by ResearchInChina co...

Automotive Camera Tier2 Suppliers Research Report, 2022-2023

1. The automotive camera market maintains a pattern of "one superpower and several great powers". Automotive cameras are used to focus the light reflected from the target onto the CIS after refractio...

Emerging Carmaker Strategy Research Report, 2023 - NIO

Emerging carmaker strategy research: NIO is deploying battery swap and sub-brands for the knockout match in 2023.In 2022, the sales surged by 32.3% year on year, being concentrated in first-tier citie...

Nissan CASE (Connectivity, Automation, Sharing and Electrification) Layout Research Report, 2022-2023

Nissan CASE research: two leverages for Dongfeng Nissan to turn the tables. Introduction: since 2020, the declining sales of Dongfeng Nissan have exposed its problems in brand influence and product co...

China Automotive Gesture Interaction Development Research Report,2022-2023

Vehicle gesture interaction research: in 2022, the installations rocketed by 315.6% year on year.China Automotive Gesture Interaction Development Research Report, 2022-2023 released by ResearchInChina...

Automotive Power Management Integrated Circuits (PMIC) Industry Report, 2023

Automotive PMIC research: the process of domestic automotive PMICs replacing foreign ones in China in the “crisis of chip shortage”. Automotive power management integrated circuits (PMIC) find broad ...

Automotive Cockpit SoC Research Report, 2023

Cockpit SoC research in 2023: Can X86 solutions returning to cockpit SoC challenge the “ARM+Google” mobile solution? This report highlights the research on the products and plans of 9 overseas and 8 ...

AI Foundation Model and Autonomous Driving Intelligent Computing Center Research Report, 2023

New infrastructures for autonomous driving: AI foundation models and intelligent computing centers are emerging. In recent years, the boom of artificial intelligence has actuated autonomous driving, ...

Automotive Microcontroller Unit (MCU) Industry Report, 2023

MCU Industry Research: Automotive high-end MCU will be still in short supply, and how OEMs can break the situation. ResearchInChina has released "Automotive Microcontroller Unit (MCU) Industry Repor...

Global and China Fuel Cell Market and Trend Research Report, 2023

Fuel Cell Industry Research: Hydrogen energy has been put on the national agenda with scenario application being rolled out.The hydrogen energy industry has been included into the national energy stra...

Global and China Automotive Smart Antenna Research Report, 2022-2023

Smart antenna research: the integration of automotive antennas and intelligent connected terminals tends to accelerate. The development trend of automotive antennas: tend to be intelligent, diversif...

Chinese Independent OEMs’ Telematics System and Entertainment Ecosystem Research Report, 2022

Vehicle telematics system research 1: the control scope is expected to expand to the entire vehicle.From January to December 2022, Chinese independent OEMs installed telematics systems in 6.42 million...

China Autonomous Shuttle Market Report, 2022-2023

Autonomous Shuttle Research: application scenarios further extend amidst policy promotion and continuous exploration Autonomous shuttles are roughly categorized into minibuses and robobuses. Minibuse...

Intelligent Cockpit Domain Control Unit (DCU) and Head Unit Dismantling Report, 2023 (1)

Dismantling of Head Unit and Cockpit Domain Control Unit (DCU) of NIO, Toyota and Great Wall Motor The report highlights the dismantling of Toyota’s MT2712-based head unit, Fisker’s Intel A2960-based ...

Automotive Vision Algorithm Industry Research Report, 2023

Research on automotive vision algorithms: focusing on urban scenarios, BEV evolves into three technology routes.1. What is BEV? BEV (Bird's Eye View), also known as God's Eye View, is an end-to-end t...

ADAS Domain Controller Key Component Trends Report, 2022

ResearchInChina researched and summarized China’s current mainstream high computing power ADAS domain controller products such as Huawei MDC and DJI ADAS domain controller prototype, and technical inf...

Automotive High-precision Positioning Research Report, 2023

High Precision Positioning Research:  four forms of mass-produced integrated high-precision positioning products With the continuous development of autonomous driving, the demand for high-precis...

Automotive AUTOSAR Platform Research Report, 2023

AUTOSAR research: CP + AP integration, ecosystem construction, and localization will be the key directions. AUTOSAR standard technology keeps upgrading, and the willingness to build open cooperation ...

2005- All Rights Reserved 京ICP备05069564号-1 京公网安备1101054484号