市場調查報告書

自動駕駛模擬:相關產業結構分析 (2019∼2020)

Autonomous Driving Simulation Industry Chain Report, 2019-2020 (I)

出版商 ResearchInChina 商品編碼 929170
出版日期 內容資訊 英文 160 Pages
商品交期: 最快1-2個工作天內
價格
自動駕駛模擬:相關產業結構分析 (2019∼2020) Autonomous Driving Simulation Industry Chain Report, 2019-2020 (I)
出版日期: 2020年03月02日內容資訊: 英文 160 Pages
簡介

自動駕駛(AD)模擬等的產業用軟體市場,有內部工程複雜,進入障礙高,開發週期長這樣的特徵。還有巨大IT企業不一定有優勢,多數的中小企業正在進入。尤其是中國的情況,由於大IT企業在技術方面也相差已開發國家有10年以上,以在現有的模擬系統的組合,及特定領域的主導權的確保作為目標。

本報告提供全球和中國的自動駕駛 (AD) 模擬的相關產業結構相關分析,AD模擬的特徵和主要的相關技術,產業的關聯結構,整合型平台/車輛運動模擬的領域的主要企業 (簡介,主要產品,企業發展情形等)等資訊彙整。

第1章 自動駕駛 (AD) 模擬

  • 模擬技術
    • 概要
    • 汽車模擬的促進因素
  • AD的模擬與實驗
    • AD模擬/實驗和其手法
    • 對AD實驗必要的計算機模擬
    • AD模擬、軟體的分類
    • 方案為基礎的ADAS/AD實驗、檢驗工具、連鎖
    • AD模擬:相關產業結構
    • AD模擬的內容
    • AD系統的仿真模型
    • 模擬/檢驗系統的結構
    • Renault的AD模擬/檢驗連鎖
    • AD模擬的課題
  • AD模擬的分類
    • 道路、氣象模擬
    • 交通情形模擬(交通量模擬)
    • 感測器模擬
    • 車輛運動模擬
    • 模擬、系統、接口
    • 分散式模擬、平台

第2章 整合模擬、平台和企業分析

  • 模擬、平台:概要
    • 模擬、平台的一般結構
    • IT企業和傳統模擬公司間的,圍繞模擬、平台的競爭
  • ANSYS
  • Siemens
  • NVIDIA Simulation Platform
  • Gazebo
  • Carla
  • China Automotive Technology and Research Center Co., Ltd. (CATARC)
  • China Automotive Engineering Research Institute Co., Ltd. (CAERI)
  • Baidu Apollo Distributed Simulation Platform
  • Tencent TAD Sim
  • PanoSim
  • AirSim
  • 51WORLD
  • 其他企業

第3章 車輛運動模擬

  • 車輛運動模擬:概要
  • MATLAB / Simulink
  • Simpack
  • TESIS DYNAware
  • IPG Carmaker
  • AVL

本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

目錄

Autonomous driving (AD) simulation: a market impossible to be ruled by IT giants

After the pioneers Baidu and Tencent in the AD simulation market, Huawei Technologies Co., Ltd follows suit and forays into it, getting small- and medium-sized players cornered.

In this report, conclusions are drawn from our insights into AD simulation.

As manufacturing is growing digital and transferring to a software-enabled industry, industrial software becomes the heart of digital manufacturing, so does for the automotive sector. Industrial software tends to be a platform facilitating industrial digitalization, networking and intelligent transformation and where small applications will run.

Industrial software segments feature complex processes, rather high thresholds and long cycles. For the IT giants, they have neither first-mover advantage nor much late-developing advantage. AD simulation software is also subject to industrial software, into which IT firms set foot and will find their incompetence, while the traditional simulation tycoons not only excel in simulation of auto parts but spare no effort in AD simulation.

It is mentioned in our study a year ago that: traditional simulation leaders keep expanding through mergers and acquisitions, boasting dozens to hundreds of product varieties that have been found in dozens of industries. For instance, ANSYS leads the pack in the CFD market, develops embedded codes, beefs up chip packaging design, and enriches internal combustion engine (ICE) simulation products through more than ten acquisitions of peers in the industry.

In 2019, ANSYS acquired British Granta Design, and American Utah-based 3DSIM, a developer of additive manufacturing (3D printing) simulation technologies. 3DSIM brings ANSYS the sole complete additive manufacturing (AM) process inside the industry. Granta Design helps to have the product portfolios of ANSYS applied to key fields. Granta Design provides customers with all kinds of important material data and enables them to visit Granta's material intelligent database system. Granta Design has products including Granta MI, a system used for enterprise material information management, and CES Slector that allows the user to grope for influence of different materials on its product behavior. Granta Design has a broad range of clients such as Airbus, General Motors, Emerson, Lockheed Martin, NASA, Saudi Aramco, and Rolls-Royce.

Traditional simulation giants builds up autonomous driving simulation technologies

Just like automakers striving to be mobility providers and recruiting software engineers aggressively, the traditional simulation companies are also improving their weaknesses. ANSYS purchased Optics in 2018 and strengthened sensor (LiDAR, camera, radar, etc.) simulation technologies, becoming a real blockbuster in the AD simulation market.

Automated Driving Toolbox of MathWorks R2019b version is added with 3D simulation support and fulfills the integrated simulation of Simulink model with the camera, LiDAR or radar sensor model in Unreal Engine, rapidly partitioning the 3D point cloud data from LiDAR.

In February 2019, Vector acquired TESIS GmbH. TESIS DYNA4 starts to be fully integrated with Vector's product lineup. The latest version of TESIS DYNA4 is added with single vertical scanning LiDAR model, supportive for the geo-reference road network of World Geodetic System WGS84, and used for simulation of GPS receiving and V2X.

In May 2019, IPG unveiled CarMaker 8.0 and rolled out sensor model LiDAR RSI, Camera RSI camera model additional with "to gain semantic segmentation image data" feature, allowing introduction and use of road network from the OpenDrive format.

The most professional Chinese testing institutions team up with overseas simulation companies to build AD simulation laboratories and provide services to domestic customers by leveraging world's state-of-the-art technologies. In February 2019, China Automotive Technology & Research Center Co., Ltd (CATARC) collaborated with IPG on building driving scenario simulation joint laboratory. In November 2019, China Automotive Engineering Research Institute Co., Ltd. (CAERI) partnered with Hexagon, NI and Konrad Technologies to jointly set up i-VISTA intelligent connected vehicle joint simulation and test laboratory.

Why can't IT giants rule the autonomous driving simulation market?

Although they are competitive in simulation software development, distributed computing, scene building, chip research and development, etc., IT giants have shortcomings as follows:

As far as autonomous driving hardware is concerned, Chinese IT giants are left at least ten years behind foreign leading companies. As for autonomous driving software technology, there is a narrow gap between Chinese and foreign brands, but a wide gap of more than a decade particularly in chassis and chip. The absence of rich data about core components of vehicle and technical accumulation make it impossible to control the vehicle accurately.

With regard to automotive simulation technology, Chinese IT giants are left dozens of years behind foreign leading companies. Automotive simulation is a fusion of technologies about computer graphics, multimedia, sensors, optics & display, materials, electronic semiconductors, kinetics, to name a few. Most Chinese IT firms are only familiar with a few disciplines.

Foreign simulation leaders has decades of rich experience in developing customers. Once an automotive simulation client selects a certain simulation technology, it is hard to change. With loyal clientele, the traditional simulation vendors keep abreast of the real demand in real time and convert it into products and services swiftly.

Autonomous driving simulation is in essence the upgrade of traditional automotive simulation. Figuratively, traditional automotive simulation has already built a 100-storey building, and only 10 storeys needing building can make it in autonomous driving simulation. Chinese IT giants can build the 101st-110th storeys but must build them on the already 100-storey, and they are enslaved. Wishing for a new building, they have to start from scratch.

So, it is useless for Chinese IT giants (except Huawei) to rebuild a simulation technology system. Even if it succeeds in building its own simulation software system, Huawei will apply the system in specific field rather than dominate the market.

Where are the opportunities for Chinese AD simulation competitors?

The aforementioned big platform and small application are the trend of industrial software (incl. simulation software). Baidu and Tencent seem to be competent enough to make big platform, but they are impossible to make a fresh start and have no choice but to join the existing simulation technology system. Tencent and Baidu with superiorities in cloud platform and HD map are improving traditional simulation technologies and products through all-round cooperation with traditional simulation technology providers on the one hand and using the newest AI and cloud computing technologies on the other hand.

For example, Baidu is improving its weakness in dynamics simulation amid introducing AADS system as concerns the 'realness' of simulation.

In July 2019, Apollo was souped up to 5.0 edition with the addition of vehicle dynamics models. Apollo 5.0 has the vehicle dynamics modeling approach (constraints in the model's complexity, precision, transferability and scalability, etc.) upgraded to the machine learning based Apollo dynamics model (high complexity, high accuracy, etc.). It is said by Baidu that the errors take a nosedive of 80% compared with the traditional modeling outcome.

The most advanced method of simulation system is to create driving scenes by using games engine. However, the CG (Computer Graphics) from the games engine rendering differs from the real scene shooting in richness and truth, degrading the performance of CG-trained autonomous driving algorithms in real scene. The AADS system, jointly developed by the University of Maryland, Baidu Research Institute and the University of Hong Kong, not only cuts the testing costs of simulation system considerably but undergoes a substantive leap in realness and scalability.

Except IT tycoons, small and medium simulation tech firms are supposed to give up big platform poise and transfer to focus on small application as an integral of the platform, in a bid to get the platform enriched and flexible use.

Beyond simulation platform, there are the AD simulation segments such as road environment simulation, traffic scene simulation, weather simulation, sensor simulation and facsimile system interface, to all of which the small and medium players can access. The opportunities here will be seen in our to-be-soon research report - Autonomous Driving Simulation Industry Chain Report, 2019-2020 (II).

Table of Contents

1. Autonomous Driving (AD) Simulation

  • 1.1 Simulation Technology
    • 1.1.1 Overview
    • 1.1.2 Drivers for Automotive Simulation
  • 1.2 AD Simulation and Test
    • 1.2.1 AD Simulation Test and Methods
    • 1.2.2 AD Test Needs Computer Simulation
    • 1.2.3 Classification of AD Simulation Software
    • 1.2.4 Scenario-based ADAS/AD Test and Verification Tool Chain
    • 1.2.5 Structure of AD Simulation Industry Chain
    • 1.2.6 Contents of AD Simulation
    • 1.2.7 AD System Simulation Model
    • 1.2.8 Composition of Simulation Test System
    • 1.2.9 Renault AD Simulation Tool Chain
    • 1.2.10 Challenges to AD Simulation
  • 1.3 Segmentation of AD Simulation
    • 1.3.1 Road and Weather Simulation
    • 1.3.2 Traffic Scene Simulation (Traffic Flow Simulation)
    • 1.3.3 Sensor Simulation
    • 1.3.4 Vehicle Dynamics Simulation
    • 1.3.5 Simulation System Interfaces
    • 1.3.6 Distributed Simulation Platform

2. Integrated Simulation Platform and Company Study

  • 2.1 Introduction to Simulation Platform
    • 2.1.1 Typical Composition of Simulation Platform
    • 2.1.2 Competition in Simulation Platform between IT Firms and Traditional Simulation Companies
  • 2.2 ANSYS
    • 2.2.1 Profile
    • 2.2.2 ANSYS' Acquisition of OPTIS
    • 2.2.3 ANSYS' Cross-industry Acquisitions to Improve Simulation Industry Chain
    • 2.2.4 Background of the Acquired Companies by ANSYS
    • 2.2.5 ANSYS' More Input in Operation and R&D
    • 2.2.6 AD Solutions and Products of ANSYS
    • 2.2.7 Significance of ANSYS' Acquisition of OPTIS
    • 2.2.8 ANSYS 2019 R3
    • 2.2.9 ANSYS SCADE
    • 2.2.10 Partners of ANSYS
    • 2.2.11 Dynamics in ANSYS' Collaborations
  • 2.3 Siemens
    • 2.3.1 AD Simulation Layout
    • 2.3.2 Major Products
    • 2.3.3 Siemens' Acquisition of TASS
    • 2.3.4 Functional Features of PreScan
    • 2.3.5 AD Simulation Use of PreScan
    • 2.3.6 Running Process of PreScan
    • 2.3.7 Sensor Types and Some Scenarios Enabled by PreScan
    • 2.3.8 External Tools and Software Favored by Prescan
    • 2.3.9 Scenario Sources Favored by Prescan
  • 2.4 NVIDIA Simulation Platform
    • 2.4.1 NVIDIA Drive Constellation
    • 2.4.2 Attributes of NVIDIA Drive Constellation
    • 2.4.3 Data Exchange between Drive Constellation and Target Vehicle
    • 2.4.4 DRIVE Constellation and DRIVE Sim
    • 2.4.5 NVIDIA Simulation Platform Composition
    • 2.4.6 Broad Partnership
  • 2.5 Gazebo
    • 2.5.1 Open Simulation Platform -- Gazebo
    • 2.5.2 Functionality and Use of Gazebo
    • 2.5.3 Merits of Gazebo
  • 2.6 Carla
    • 2.6.1 Introduction to Carla
    • 2.6.2 Carla Building of Different Scenarios
    • 2.6.3 Latest Version of Carla
    • 2.6.4 Functional Highlights of Carla
  • 2.7 China Automotive Technology and Research Center Co., Ltd. (CATARC)
    • 2.7.1 Profile of CATARC
    • 2.7.2 CATARC Simulation Platform
    • 2.7.3 CATARC Scene Platform
    • 2.7.4 Collaboration between CATARC and IPG on Building a Driving Scene Simulation Job Lab
  • 2.8 China Automotive Engineering Research Institute Co., Ltd. (CAERI)
    • 2.8.1 CAERI Layout in Simulation Test Platform Tool Chain
    • 2.8.2 i-Collector
    • 2.8.3 i-Transfomer and i-Creator
    • 2.8.4 ADAS HIL Integration and Test Services
    • 2.8.5 Building AD Simulation Data Crowdsourcing & Testing Service Cloud Platform
  • 2.9 Baidu Apollo Distributed Simulation Platform
    • 2.9.1 Apollo Simulation Platform
    • 2.9.2 Apollo Simulation Engine
    • 2.9.3 ApolloScape
    • 2.9.4 Apollo Control-in-the-loop
    • 2.9.5 Apollo Vehicle Dynamics Model Simulation
    • 2.9.6 AADS System
    • 2.9.7 Two Superiorities of AADS
    • 2.9.8 Collaborations with Apollo Simulation Platform
  • 2.10 Tencent TAD Sim
    • 2.10.1 Autonomous Driving Layout of Tencent
    • 2.10.2 TAD Sim Simulation Platform
    • 2.10.3 Characteristics of TAD Sim Simulation Platform
    • 2.10.4 High-fidelity Scenarios of TAD Sim
    • 2.10.5 Sensor Simulation of TAD Sim Simulation Platform
    • 2.10.6 Simulation of Complex Road Conditions
    • 2.10.7 Cloud Acceleration Simulation, CIVS (Cooperative Vehicle Infrastructure System) Simulation, 3D City Rebuilding
    • 2.10.8 Application of TAD Sim Simulation Platform
  • 2.11 Panosim
    • 2.11.1 Profile
    • 2.11.2 Major Products
    • 2.11.3 Key Customers
    • 2.11.4 PanoSim Based on Physical Model and Numerical Simulation
    • 2.11.5 PanoSim Interface and Functions
    • 2.11.6 PanoSim Used to Create Simulation Experiment Flow
    • 2.11.7 PanoSim 3.0 Added with Radar Model and GPS Physical Model
    • 2.11.8 PanoSim 3.0 - V2X and True Value Sensor Function Upgrade
    • 2.11.9 PanoSim3.0 Optimized Simulink Model
  • 2.12 AirSim
    • 2.12.1 Open Simulation Platform -- AirSim
    • 2.12.2 AirSim on Unity
    • 2.12.3 Features of AirSim Simulator
  • 2.13 51World
    • 2.13.1 Profile of 51WORLD
    • 2.13.2 51Sim-One
    • 2.13.3 In-built Vehicle Dynamics System
    • 2.13.4 Application of 51Sim-One
    • 2.13.5 AD Simulation Partners
    • 2.13.6 51WORLD EC (Earth Clone)
  • ........

3. Vehicle Dynamics Simulation

  • 3.1 Introduction to Vehicle Dynamics Simulation
  • 3.2 MATLAB/Simulink
    • 3.2.1 Introduction to Mathworks and Simulink
    • 3.2.2 Product Packets
    • 3.2.3 Simulink-based AEB and FCW System
    • 3.2.4 ADST
    • 3.2.5 Various Models of Simulink
    • 3.2.6 Driving Scenario Designer
    • 3.2.7 Vehicle Dynamics Blockset
    • 3.2.8 Key Modules of Vehicle Dynamics Blockset
    • 3.2.9 Cases of Use for Close-loop Simulation Test
    • 3.2.10 Use in Voyage
    • 3.2.11 New Features
  • 3.3 Simpack
    • 3.3.1 Introduction to Simpack
    • 3.3.2 Simpack Real-time Simulation Tools
    • 3.3.3 Simpack Automotive
    • 3.3.4 Simpack Automotive Modeling Features
    • 3.3.5 Simpack Use in ADAS
    • 3.3.6 New Functions
  • 3.4 TESIS DYNAware
    • 3.4.1 Profile of TESIS
    • 3.4.2 TESIS DYNAware
    • 3.4.3 veDYNA Real-time Simulation of Vehicle Dynamics
    • 3.4.4 ve-DYNA Model
    • 3.4.5 DYNA4 Software
    • 3.4.6 DYNA4 Software Functionality
    • 3.4.7 DYNA4 Simulation Scene
    • 3.4.8 Latest Trends of DYNA4
    • 3.4.9 New Functions of DYNA4
  • 3.5 IPG Carmaker
    • 3.5.1 Profile of IPG Carmaker
    • 3.5.2 Products Backed by IPG Carmaker
    • 3.5.3 Characteristics of IPG Carmaker
    • 3.5.4 Roll-out of IPG Carmaker 8.0
    • 3.5.5 Latest News about IPG
  • 3.6 AVL
    • 3.6.1 Profile
    • 3.6.2 AVL CRUISE
    • 3.6.3 AVL Automotive Test Simulation Platform
    • 3.6.4 AVL model.Connect
    • 3.6.5 Features and Application of AVL model.Connect
    • 3.6.6 AVL DRIVINGCUBE
    • 3.6.7 AVL Autonomous Driving Simulation Developments