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市場調查報告書

自動駕駛仿真產業鏈:2020-2021(二)

Autonomous Driving Simulation Industry Chain Report, 2020-2021 (II)

出版商 ResearchInChina 商品編碼 1015685
出版日期 內容資訊 英文 203 Pages
商品交期: 最快1-2個工作天內
價格
自動駕駛仿真產業鏈:2020-2021(二) Autonomous Driving Simulation Industry Chain Report, 2020-2021 (II)
出版日期: 2021年05月14日內容資訊: 英文 203 Pages
簡介

根據我國《2020年自動駕駛仿真藍皮書》,目前自動駕駛算法測試的分佈大約是90%在仿真平台上,9%在測試場上,1%在公共道路上。我會的。隨著仿真技術的進步和普及,業界的目標是在仿真平台上進行99.9%的測試,在封閉場景中進行0.09%的測試,在實際道路上進行0.01%的測試。自動駕駛下半年,商業化將帶動測試需求激增,可能引發仿真行業新的重組。

本報告調查了自動駕駛仿真產業鏈,提供了概覽、現狀和趨勢、主要公司等信息。除了《自動駕駛仿真產業鏈:2020-2021(一)》中描述的閉環平台仿真和車輛動力學仿真外,自動駕駛仿真還包括交通流仿真、場景仿真、傳感器仿真等模塊。 .

目錄

第4章道路氣象環境與交通場景模擬

  • 交通場景模擬(交通流模擬)
    • 概述
    • 分類
    • 公司
    • PTV-VISSIM
    • CorSim
    • 參數
    • 變形金剛
    • 愛姆生
    • 相撲
  • 構建虛擬場景(天氣、道路、交通等)
    • 概述
    • 道路環境模擬和氣象環境模擬
    • 虛擬場景搭建公司概況
    • ESI Pro-SiVIC
    • rFpro
    • 科納塔
    • 並行域
    • 人工智能
    • 應用直覺
    • Ansible 運動
    • 團結
    • 向量零路跑者
    • 城市引擎
    • VTD

第五章傳感器仿真

  • 概述
  • 示例
  • 公司
  • MonoDrive
    • 個人資料
    • 傳感器模擬器
    • 模擬器性能
    • 測試模式
    • 產品工作流程
    • 相機模擬器
  • 右鉤
    • 個人資料
    • 模擬概述
    • 支持的傳感器
    • 模擬工作流程
    • 解決方案
  • 元本
    • 個人資料
    • 模擬平台
    • 合作活動
    • 贏了
  • OTSL
    • 個人資料
    • 宇宙模擬
    • 合作活動

第 6 章仿真接口和 HIL

  • 仿真係統界面概述
  • 仿真係統接口分類
  • 硬件在環 (HIL) 仿真概述
  • HIL仿真公司
  • 國家儀器 (NI)
    • 個人資料
    • 軟件連接方案
    • 模擬收入 (2023E)
    • 行業應用
    • 車輛雷達測試系統 (VRTS)
    • 模塊化測試平台
    • 攝像頭和 V2X HIL 測試
    • ADAS 傳感器與 HIL 測試解決方案集成
    • 動力總成 HIL 測試解決方案
  • ETAS
    • 個人資料
    • 測試和驗證服務-LABCAR
    • 測試驗證服務-COSYM協同仿真平台
  • 向量
    • 個人資料
    • 閉環測試系統
    • HIL 應用案例
    • VT系統
  • dSPACE
    • 個人資料
    • 組合方案
    • 實時仿真係統解決方案
    • 傳感器模擬
    • ASM 用於 ADAS 和自動駕駛 (AD)
    • 傳感器型號
    • 傳感器模型集成示例
    • 雲解決方案
    • 協作機制
    • 合作夥伴
    • 力學

第 7 章趨勢和預測

目錄

In addition to simulation closed-loop platform and vehicle dynamics simulation mentioned in the Autonomous Driving Simulation Industry Chain Report, 2020-2021 (I), autonomous driving simulation also involves traffic flow simulation, scenario simulation and sensor simulation modules. The Autonomous Driving Simulation Industry Chain Report, 2020-2021 (II) sorts through companies in these areas.

Acquisitions (or mergers and acquisitions) are, beyond doubt, a shortcut for companies to better technology layout. Autonomous driving simulation is no exception. Ansys' acquisition of the optical simulation software provider OPTIS and Siemens' purchase of TASS have been milestones in their development histories of autonomous driving simulation technology.

The more mature the autonomous driving simulation industry becomes, the higher barriers the industry poses. Technology and capital walls put up by simulation giants have been a big hindrance to the growth of start-ups. Players that just specialize in their own field may end up with being acquired or introducing external support. They cannot escape from tycoons at last.

Acquiring these specialized leaders has been an easy way for giants to perfect their layout

1. VectorZero, the owner of the scenario editor RoadRunner, was acquired by MathWorks, an integrated simulation platform, and its simulation tools were included in MATLAB/Simulink product system.

RoadRunner owned by VectorZero is a scene editor. It can create environments and roads, generate complex road networks composed of roundabouts, intersections and bridges, and custom traffic signs and markings.

Benefits of RoadRunner:

  • 1. A variety of editing tools: road tools, junction tools, lane tools, marking tools, prop tools, etc.;
  • 2. Quick 3D scene modeling: RoadRunner Asset Library lets users quickly populate their 3D scenes with 3D models.

MathWorks just settles on RoadRunner's 3D scene capabilities.

In April 2020, the integrated simulation platform MathWorks acquired VectorZero, and brought RoadRunner tools for designing 3D scenes for automated driving simulation, into its MATLAB/Simulink product system.

In May 2020, MATLAB R2020a Version added RoadRunner tools to Automated Driving Toolbox.

2. The integrated simulation platform Spectris plc acquired VI-grade (vehicle dynamics simulation) and RightHook (sensor simulation).

In July 2018, Spectris plc acquired VI-grade, a vehicle dynamics and driving simulator player, for a foray into the vehicle testing and simulation field.

In February 2019, Spectris plc bought RightHook, a sensor simulation firm, and then merged it into VI-grade.

Benefits of RightHook:

  • 1. Provide a complete simulation tool chain including RightWorld and RightWorldHD, RightWorldHIL;
  • 2. Enable HD map-based simulation, and rebuild the whole simulation environment according to the HD maps used by autonomous driving companies. The test environment is real driving environment.

VI-CarRealTime, VI-grade's vehicle dynamics model, provides a set of dynamics simulation services such as hardware/software in the loop.

In November 2020, VI-grade introduced VI-WorldSim that provides urban and public road test environments for ADAS and autonomous vehicles. VI-WorldSim features include traffic, pedestrians, lighting, weather, and sensors to enable users to create and test scenarios for vehicle development programs through an intuitive and easy-to-use desktop editor.

Noticeably, for this product, RightHook provides integrated visual environment for driving simulators, which means the two companies have merged in terms of operation and products.

Start-ups double down on financing, hoping to change the fate of "being acquried".

1. Applied Intuition raised USD125 million.

Applied Intuition was founded by Qasar Younis and Peter Ludwig (former workers of Google) in 2017. The company recorded roughly USD26 million in revenue in 2020. The edge of Applied Intuition lies in the ability to use real/synthesized data to build complex scene interactions in a short time and generate thousands of permutations to cover edge scenarios. Meanwhile, in the simulation process, the dashboard of the virtual vehicle can display "the impact of virtual intersections and obstacles on vehicle acceleration and passenger comfort", and other information.

On October 22, 2020, Applied Intuition raised USD125 million in a Series C funding round led by Lux Capital, Andreessen Horowitz, and General Catalyst, which took its market capitalization to USD1.25 billion.

2. The scenario simulation startup Parrallel Domain raised USD11 million in a Series A funding round where Toyota was a co-investor.

Parallel Domain was founded by Kevin McNamara (with a background in Apple autonomous driving technology) in 2017. Parallel Domain can synthesize a variety of scenes (e.g., day, night, fog, rain and city) for sensors (including LiDAR and camera), and also can embed complex elements (e.g., traffic lights, vehicles, pedestrians and animals) in scenes. Its simulation platform provides abundant metadata for users to test various new sensors and technical configurations.

In October 2020, Parallel Domain raised USD11 million in a Series A funding round led by Foundry Group and co-invested by Calibrate Ventures, Costanoa Ventures, Ubiquity Ventures and Toyota AI Ventures.

3. The scenario simulation company Cognata added partners including Hyundai Mobis, Atlatec and Ouster between 2020 and 2021 for accelerating commercialization of products.

Combining artificial intelligence, deep learning and computer vision, Cognata reproduces cities on its 3D simulation platform, providing customers with a range of test scenarios that simulate real-world test driving. In 2020, Cognata increased several partners, gathering pace in product application and variety.

  • 1. In November 2020, Cognata teamed up with Atlatec to support Atlatec's HD maps on the Cognata simulation platform, providing customers with the ability to extend the catalogs of accurate environments available for large-scale virtual validation;
  • 2. In January 2021, Cognata and Ouster partnered up in order to develop an accurate virtual LiDAR model in Cognata's simulation software.

According to the 2020 Blue Paper on Autonomous Driving Simulation of China, the current distribution of autonomous driving algorithm tests is as follows: around 90% tests are completed on simulation platforms, 9% in test fields and 1% on public roads. As simulation technology advances and becomes widespread, the industry aims at 99.9% tests carried out on simulation platforms, 0.09% in closed scenarios and 0.01% on real roads. In the second half of autonomous driving, the commercial use will bring soaring demand for testing, which may catalyze a new round of shuffle in the simulation industry.

Table of Contents

4 Road and Weather Environments and Traffic Scene Simulation

  • 4.1 Traffic Scene Simulation (Traffic Flow Simulation)
    • 4.1.1 Overview
    • 4.1.2 Classification
    • 4.1.3 Companies
    • 4.1.4 PTV-VISSIM
      • 4.1.4.1 Profile and Main Products
      • 4.1.4.2 Simulation Solution: VISSIM
      • 4.1.4.3 VISSIM Platooning Model
      • 4.1.4.4 VISSIM Product Updates
      • 4.1.4.5 Application of VISSIM in Autonomous Driving
    • 4.1.5 CorSim
      • 4.1.5.1 Overview of Products
      • 4.1.5.2 Version Updates
    • 4.1.6 PARAMICS
      • 4.1.6.1 Profile
      • 4.1.6.2 Features
      • 4.1.6.3 Version Updates
    • 4.1.7 Transmodeler
      • 4.1.7.1 Profile
      • 4.1.7.2 Main Features
      • 4.1.7.3 Historical Versions
      • 4.1.7.4 Version Updates
      • 4.1.7.5 Lane-level Networks
    • 4.1.8 AIMSUN
      • 4.1.8.1 Profile
      • 4.1.8.2 Aimsun Next
      • 4.1.8.3 Aimsun Next: Features
      • 4.1.8.4 Aimsun Next: Version Updates
      • 4.1.8.5 Aimsun Next: Functional Module Configurations in New Versions
    • 4.1.9 SUMO
      • 4.1.9.1 Profile
      • 4.1.9.2 Functional Modules
      • 4.1.9.3 Features
      • 4.1.9.4 Version Updates
  • 4.2 Construction of Virtual Scenes (Weather, Roads, Traffic, etc.)
    • 4.2.1 Overview
    • 4.2.2 Road Environment Simulation & Weather Environment Simulation
    • 4.2.3 Overview of Virtual Scene Construction Companies
    • 4.2.4 ESI Pro-SiVIC
      • 4.2.4.1 Profile of ESI
      • 4.2.4.2 Acquisitions and Integrations of ESI
      • 4.2.4.3 Product Distribution of ESI Group
      • 4.2.4.4 Profile of Pro-SiVIC
      • 4.2.4.5 Application of Pro-SiVIC
      • 4.2.4.6 Operation Process and Element Library of Pro-SiVIC
      • 4.2.4.7 Historical Versions
      • 4.2.4.8 Version Updates
    • 4.2.5 rFpro
      • 4.2.5.1 Profile
      • 4.2.5.2 ADAS & Autonomous Solutions
      • 4.2.5.3 Autonomous Driving Testing in VR and Introduction of Map Models
      • 4.2.5.4 Digital Road Model
      • 4.2.5.5 Virtual Environment Cooperated with NVIDIA
      • 4.2.5.6 Partners
    • 4.2.6 Cognata
      • 4.2.6.1 Profile
      • 4.2.6.2 Simulation Platform
      • 4.2.6.3 Large-scale Scene Generation
      • 4.2.6.4 Dynamics in Cooperation
    • 4.2.7 Parallel Domain
      • 4.2.7.1 Profile
      • 4.2.7.2 Simulation Platform
      • 4.2.7.3 Series A Funding Round
    • 4.2.8 AAI
      • 4.2.8.1 Profile
      • 4.2.8.2 Main Products & Solutions
      • 4.2.8.3 Application
      • 4.2.8.4 Replicar
      • 4.2.8.5 Scene Cloning and Extraction
      • 4.2.8.6 Sensor Simulation
      • 4.2.8.7 Dynamics in Cooperation
    • 4.2.9 Applied Intuition
      • 4.2.9.1 Profile
      • 4.2.9.2 Simulation Platform
      • 4.2.9.3 Application Cases
      • 4.2.9.4 Toyota & Applied Intuition
      • 4.2.9.5 Recent Dynamics
    • 4.2.10 Ansible Motion
      • 4.2.10.1 Profile
      • 4.2.10.2 Solutions
      • 4.2.10.3 Solutions for Passenger Cars
    • 4.2.11 UNITY
      • 4.2.11.1 Profile
      • 4.2.11.2 Unity SimViz
      • 4.2.11.3 AirSim on Unity
    • 4.2.12 VectorZero-RoadRunner
    • 4.2.13 CityEngine
      • 4.2.13.1 Profile
      • 4.2.13.2 Version Updates
    • 4.2.14 VTD
      • 4.2.14.1 MSC Software
      • 4.2.14.2 Profile of VTD
      • 4.2.14.3 VTD Components
      • 4.2.14.4 VTD Application
      • 4.2.14.5 OpenDRIVE Scene Editor

5 Sensor Simulation

  • 5.1 Overview
  • 5.2 Examples
  • 5.3 Companies
  • 5.4 MonoDrive
    • 5.4.1 Profile
    • 5.4.2 Sensor Simulator
    • 5.4.3 Simulator Performance
    • 5.4.4 Test Mode
    • 5.4.5 Product Workflow
    • 5.4.6 Camera Simulator
  • 5.5 RightHook
    • 5.5.1 Profile
    • 5.5.1 Overview of Simulation
    • 5.5.2 Supported Sensors
    • 5.5.3 Simulation Workflow
    • 5.5.4 Solutions
  • 5.6 Metamoto
    • 5.6.1 Profile
    • 5.6.2 Simulation Platform
    • 5.6.3 Cooperation Events
    • 5.6.4 Acquired
  • 5.7 OTSL
    • 5.7.1 Profile
    • 5.7.2 COSMOSIM
    • 5.7.3 Cooperation Events

6 Simulation Interface and HIL

  • 6.1 Overview of Simulation System Interface
  • 6.2 Classification of Simulation System Interface
  • 6.3 Overview of Hardware-in-the-Loop (HIL) Simulation
  • 6.4 HIL Simulation Companies
  • 6.5 National Instruments (NI)
    • 6.5.1 Profile
    • 6.5.2 Software-connected Solutions
    • 6.5.3 Simulation Revenue, 2023E
    • 6.5.4 Industry Application
    • 6.5.5 Vehicle Radar Test System (VRTS)
    • 6.5.6 Modular Test Platform
    • 6.5.7 Camera and V2X HIL Test
    • 6.5.8 ADAS Sensor Integrated with HIL Test Solution
    • 6.5.9 Powertrain HIL Test Solution
  • 6.6 ETAS
    • 6.6.1 Profile
    • 6.6.2 Testing and Verification Services-LABCAR
    • 6.6.3 Testing and verification services-COSYM Co-simulation Platform
  • 6.7 Vector
    • 6.7.1 Profile
    • 6.7.2 Closed-loop Test System
    • 6.7.3 HIL Application Cases
    • 6.7.4 VT System
  • 6.8 dSPACE
    • 6.8.1 Profile
    • 6.8.2 Solution Combinations
    • 6.8.3 Real-time Simulation System Solutions
    • 6.8.4 Sensor Simulation
    • 6.8.5 ASM Used in ADAS and Automated Driving (AD)
    • 6.8.6 Sensor Model
    • 6.8.7 Sensor Model Integration Examples
    • 6.8.8 Cloud Solutions
    • 6.8.9 Dynamics in Cooperation
    • 6.8.10 Partners
    • 6.8.11 Dynamics

7 Trends and Forecast