Global Autonomous Driving Simulation and Virtual Test Industry Chain Report, 2018-2019

Global Autonomous Driving Simulation and Virtual Test Industry Chain Report, 2018-2019

It is in this report that the autonomous driving simulation industry is analytically expounded, ranging from simulation platform, vehicle dynamics simulation, sensor simulation, scenario simulation to scenario library.

Simulation technologies seem afar to people’s lives and are hard to understand for them, but it is a key domain in which breakthroughs are to be made for automotive sector, even intelligent manufacturing in China and it deserves heavy investments.

Simulation technology is the simulation model technology that reveals the system behaviors and process by way of simulation experiments and numerical computing with simulation hardware and software. Simulation technology found initial application in the early 20th century, taking example for the building of water conservancy model in laboratories for research of hydraulics. In 1940s-1950s, the burgeoning aviation & aerospace and atomic energy technologies conduced to advances in simulation technology. In 1960s, the computer technology flourished and the advanced simulation tools became available, which expedited evolution of simulation technology.

From 1990s on, simulation and digital virtualization technologies has been an integral crucial to the R&D of automobiles. The advanced idea to develop cars based on mathematical model and digital simulation has prevailed among the automakers worldwide.

The vehicle R&D becomes ever sophisticated as cars are going smarter and smarter, causing higher costs of vehicle development and a prolonged development cycle. Lots of new technologies about vehicle security are subject to external environment and test security restraints and are hard to be carried out effectively. Yet, the traditional means for R&D, tests and validation are out of date.

For adequate security validation, autonomous driving requires a great deal of scalable simulation testing service (billions of km and even to tens of billions of km). Actually, the real road test is featured with low efficiency, and many automakers favor selecting autonomous driving simulation tests.

It is pointed out by a guest speaker on an automobile forum that, 90 percent of autonomous driving tests will be done by simulation, 9 percent done in test fields, and 1 percent on real roads.

The so-called autonomous driving simulation tests are to test autonomous cars with technologies such as sensor simulation, vehicle dynamics simulation, advanced graphics processing, traffic flow simulation, numerical simulation and road modeling, and with algorithms to build the comparatively real driving scenarios.

Such processes must be undergone to develop an autonomous driving system, as software simulation, hardware in the loop (HiL), vehicle in the loop (VIL), indoors lab tests, outdoor test field, and to ultimately the massive tests on public roads.

The Vehicle Simulation Industry Dominated by German and American Companies

There are dozens of simulation test companies around the globe, among which America ranks first by the number of companies but Germany boast most simulation firms in the automotive sector.

It can be seen from the development course of simulation industry that new opportunities emerge all the time and the emerging companies have sprung up incessantly. Simulation tycoons have grown ever competitive through mergers and acquisitions and have developed dozens of and even hundreds of product categories which are applied in tens of industries.

Through over ten acquisitions on companies inside and outside the industry, ANSYS dominates the CFD market, develops the embedded codes, beefs up chip encapsulation design, and enriches internal combustion engine simulation products. ANSYS purchased OPTIS in 2018 and improved the simulation technologies about sensors like LiDAR, camera and radar.

The potentiality of the autonomous driving simulation market has allured the inrush of many enterprises outside the automotive sector.

Autonomous driving tests call for various traffic scenarios simulation tool and massive scenario library. This is a totally new market, bringing traditional simulation companies, startups and new entrants on a par, and attracting the inroads of game firms, VR/AR firms and internet firms.

In October 2018, Cognata -- the Israel-based autonomous driving simulation startup -- completed the B-round funding of $18.5 million. Cognata reproduces the cities on its 3D simulation platform by fusing artificial intelligence, deep learning and computer vision, and provides the customers with a variety of the driving test scenarios simulating the real world. AID (Autonomous Intelligent Driving GmbH) under Audi reached cooperation with Cognata.

Founded in 2015, 51VR was grown from real property market and then made its foray in automobile, education, games and other fields, and is now primarily focused on 3D simulation reconstruction. In December 2017, 51VR conducted its B-round funding of RMB210 million. For now, 51VR boasts nearly 100 talents in the automotive business covering three modules, i.e., simulated driving experience (VR car), vehicle visualization, and autonomous driving simulation platform, of which simulation platform team accounts for more than a half of its workforce.

Baidu Apollo simulation platform is in-built with simulation scenarios with HD maps in favor of multi-algorithm (sensing, planning and control) module verification, making the autonomous driving algorithm verification more rigorous. Apollo 1.5 version began to open its simulation platform, empowering autonomous driving companies like Idriverplus to significantly improve their efficiency in R&D via Apollo cloud simulation capabilities. Baidu Apollo could sell HD map, simulation platform, computing hardware ACU in future, said by Li Yanhong, the chairman of Baidu. So, simulation platform will be one of the profit points of Baidu self-driving platform.

In line with Tencent’s layout in autonomous driving, simulation platform is one of the three basic competencies; maps and simulation services will be one of three profit engines of Tencent in future. Thanks to its superiorities in game engine, virtual reality, cloud games technologies, etc., Tencent has successfully built the simulation system TAD Sim (virtual + real combination) that is capable of verifying the closed-loop simulation of all modules (like sensing, decision, control algorithm) of a real car. Tencent wishes its simulation platform can help the automakers shorten development cycle, improve development efficiency, and reduce the costs of tests.

Since simulation software involves with hundreds of product categories, into which there are many ways to go. In comparison with difficulties in chassis and vehicle chip, the (automotive) industrial software represented by autonomous driving simulation is possibly the best field where IT/AI/VR firms can make investments.

1. Introduction to Vehicle Simulation and Autonomous Driving Simulation
1.1 Overview of Simulation Technology
1.1.1 Development of Simulation Technology
1.1.2 Vehicle Simulation Technology
1.1.3 Autonomous Driving Testing and Simulation
1.1.4 Drivers for Development of Simulation Testing
1.2 Autonomous Driving Testing and Simulation
1.2.1 Simulation Technology Will be the Foundation of Autonomous Driving
1.2.2 Scenario-based ADAS/AD Testing and Verification Tool Chain
1.2.3 Composition of Autonomous Driving Simulation Industry Chain
1.2.4 Content of Autonomous Driving Simulation
1.2.5 Simulation Models of Autonomous Driving System
1.2.6 Composition of Simulation Testing System
1.2.7 How Simulation Testing Replaces Real Driving
1.2.8 Simulation Is Introduced to Replace the Real World
1.3 HIL and SIL
1.3.1 HIL Testing
1.3.2 Process of SIL Simulation
1.3.3 SIL Simulation of Volkswagen
1.3.4 SIL Simulation of BMW
1.3.5 SIL Simulation of Mercedes-Benz
2. Autonomous Driving Simulation Platforms and Companies
2.1 Introduction to Simulation Testing Platforms
2.1.1 Composition of Simulation Testing Platforms
2.1.2 Competition between Traditional Simulation Enterprises and IT Firms in Simulation Platforms
2.2 Driving Simulator
2.2.1 Features
2.2.2 ADAS and AD Testing Project
2.2.3 Combination with Other Simulation Testing Tools
2.3.1 Profile
2.3.2 Improve Simulation Industry Chain through Cross-industry Acquisitions
2.3.3 Background of Acquired Companies
2.3.4 Autonomous Driving-related Products
2.3.5 Acquisition of OPTIS
2.3.6 ANSYS SCADE Can Generate an Authentication Code that Meets the ISO26262 Standard
2.3.7 ANSYS Scade Suite—Driving Scenario Model
2.3.8 ANSYS Scade Suite- Autonomous Driving Controlled Simulation Solution
2.3.9 ANSYS Scade Suite Autonomous Driving Controlled Simulation Solution Process
2.3.10 ANSYS Autonomous Driving Controlled Simulation Solution Closed Loop
2.3.11 ANSYS Scade Suite Autonomous Driving Controlled Simulation Testing Display
2.4 TASS PreScan
2.4.1 Siemens’ Acquisition on Tass
2.4.2 Siemens’ Products in Autonomous Driving Testing
2.4.3 TASS PreScan
2.4.4 Features of New PreScan
2.4.5 Some Features of PreScan Simulation Platform
2.4.6 Sensor Types and Some Scenarios Supported by PreScan
2.4.7 External Tools and Software Supported by PreScan
2.5 NVIDIA Simulation Platform
2.6 Gazebo
2.6.1 Open Source Simulation Platform
2.6.2 Features and Usage
2.7 Carla
2.7.1 Introduction
2.7.2 Latest version
2.8 Scenario Simulation Platform of CATARC
2.8.1 Driving Simulation Platform
2.8.2 Driving Scenario Platform
2.9 Apollo Simulation Platform
2.9.1 Distributed Simulation Platform
2.9.2 Engine
2.9.3 Development Resource Dataset
2.9.4 Future Planning
2.10 Panosim
2.10.1 Profile
2.10.2 Main Products
2.10.3 Simulation Based on Physical Models and Numbers
2.10.4 Interface and Features
2.10.5 Process of Creating a Simulation Experiment with PanoSim
2.10.6 New Radar Model and GPS Physical Model
2.10.7 Feature Upgrade of V2X and True Value Sensor
2.10.8 Optimization of Simulink Model
2.10.9 Summary
2.11 AirSim
2.11.1 Profile
2.11.2 Open Source Simulation Platform
2.12 VI-grade
2.12.1 Profile
2.12.2 Product System
2.12.3 Application in the Automotive Sector
2.12.4 VI-Car RealTime: Automotive Dynamics Real-Time Simulation Tool
2.12.5 Automotive Driving Simulator: VI-DriveSim
2.12.6 VI-grade and SAGLNOMIYA
2.12.7 Applus+ IDIADA Adopts DiM250
3. Vehicle Dynamics Simulation
3.1 MATLAB/Simulink
3.1.1 Introduction to Mathworks
3.1.2 Matlab and Simulink
3.1.3 Simulink-based AEB and FCW System
3.1.4 ADST - Autonomous Driving System Kit
3.1.5 Simulink Model
3.1.6 Driving Scenario Designer
3.1.7 Vehicle Dynamics Blockset
3.1.8 Modular Driving Scenario of Vehicle Dynamics Blockset
3.1.9 Cases of Vehicle Dynamics Blockset in Closed Loop Simulation Testing
3.1.10 Application in Voyage
3.2 Simpack
3.2.1 Real-time Simulation Tool
3.2.2 Open Modeling Structure and Features
3.2.3 Features of Modeling
3.2.4 ADAS Application
3.3 TESIS DYNAware
3.3.1 Profile
3.3.2 ve-DYNA Model
3.3.3 DYNA4 Software
3.3.4 Seamless Connection between DYNA4 and enDYNA/veDYNA
3.3.5 Features of DYNA4 Software
3.3.6 Simulation Scenario of DYNA4
3.4 IPG Carmaker
3.5 AVL
3.5.1 Profile
3.5.2 Vehicle Testing Simulation Platform
3.5.3 AVL model. Connect
3.5.4 ADAS Simulation Testing Based on Actual Scenarios
4. HIL Simulation
4.1 NI
4.1.1 Automotive Solutions
4.1.2 VRTS
4.1.3 Cameras and V2X Testing
4.2 ETAS
4.2.1 HiL System
4.2.2 Simulation Solutions and Tools
4.3 Vector
4.3.1 ADAS Development Tool Chain
4.3.2 MIL Simulation Testing
4.4 dSPACE
4.4.1 Profile
4.4.2 Real-time Simulation System
4.4.3 Main Products
4.4.4 Simulation Tool Chain
5. Scenario Simulation Companies
5.1 Introduction to Scenario Library
5.1.1 Requirements of Intelligent Vehicle Simulation Testing on Traffic Scenario Simulation
5.1.2 Standardized Testing Scenarios
5.1.3 Measurement Parameters of Standardized Testing Scenarios
5.2 VTD
5.2.1 VIRES
5.2.2 VTD Simulation Tool
5.2.3 Three Standards Based on VTD Environment Simulation
5.2.4 Composition and Advantages of Open Drive Standard
5.2.5 Application of Open Drive
5.2.6 Open Drive Map Data Analysis
5.2.7 Establishment of a Map Coordinate System in Open Drive
5.2.8 Open Drive-Reference Line Construction
5.2.9 Open Drive-Lane Construction
5.2.10 Open Drive-Complete Road Models
5.2.11 Open CRG Simulation Applied in Vehicle Control, Driving Comfort, etc.
5.2.12 Open CRG- Workflow
5.2.13 Open–Scenario Standard
5.3 Pro-SiVIC
5.3.1 Profile of ESI
5.3.2 ESI’s Products
5.3.3 ESI Enhances Core Simulation Technology through Acquisition and Integration
5.3.4 Profile of Pro-SiVIC
5.3.5 Traffic Scenarios and Sensor Models of Pro-SiVIC
5.3.6 Process of Composograph Processing with Pro-ViSIC Simulation
5.3.7 Result of Composograph Processing with Pro-ViSIC Simulation
5.4 rFpro
5.4.1 Profile
5.4.2 HD Virtual Modeling for Test Fields
5.4.3 R&D of HD Virtual Modeling
5.5 Cognata
5.5.1 Profile
5.5.2 Autonomous Driving Simulation Platform
5.5.3 Cooperation with Nvidia
5.6 51VR/RealDrive
5.6.1 Profile
5.6.2 Team
5.6.3 Simulation Approaches of Autonomous Driving Simulation Platform
5.6.4 51World Strategy
5.6.5 RealDrive Aims at Autonomous Driving Simulation Testing
5.7 Parallel Domain
5.7.1 Profile
5.7.2 Simulation Platform
5.7.3 How to Generate a Virtual World
5.8 Metamoto
5.9 AAI
5.10 Applied Intuition
6. Sensor Simulation Companies
6.1 MonoDrive
6.1.1 Profile
6.1.2 Sensor Simulator
6.1.3 Process of Simulation Testing
6.2 RightHook
6.2.1 Profile
6.2.2 Playback Simulation and HD Map Simulation
6.2.3 Simulation System Based on HD Map
6.4 Claytex
7. Scenario Library
7.1 Germany GIDAS
7.2 China CIDAS
7.3 ADAC
7.4 iVista
7.5 Kunlun Plan

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