Autonomous Driving Simulation Research (Part 2): After all, it is the arena of giants

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At the beginning of 2019, Alibaba Dharma Academy announced the “Top Ten Technology Trends in 2019”. Looking back a year later, most trends still hold today. There are two related to autonomous driving:

Trend 1: Autonomous driving enters a period of calm development

Relying solely on “single-vehicle intelligence” to innovate cars will not be able to achieve ultimate unmanned driving for a long time, but it does not mean that autonomous driving has completely entered the cold winter. The technical route of vehicle-road coordination will speed up the arrival of unmanned driving. In the next 2-3 years, the commercial application of autonomous driving represented by limited scenarios such as logistics and transportation will usher in new progress. For example, commercial scenarios such as fixed-line buses, unmanned distribution, and park microcirculation will quickly land.

Trend 2: Real-time simulation of cities becomes possible, and smart cities are born

The perception data of urban public infrastructure and the urban real-time pulsating data flow will be converged on the large computing platform. The development of computing power and algorithms will promote the real-time integration of unstructured information such as video and other structured information, and real-time urban simulation will become possible. Intelligence will be upgraded to global intelligence. In the future, there will be more forces for the research and development of urban brain technology and applications. A smart city with full time and space perception, full element linkage, and full cycle iteration will be born above the physical city. The development of autonomous driving simulation is closely related to the development of the entire autonomous driving industry. In the past two years, the pace of development of autonomous driving has slowed down, and start-ups have faced unprecedented challenges. The same is true for startups in the field of autonomous driving simulation. RightHook, a sensor simulation company, has not updated its news for two years, and there are basically no new autonomous driving simulation startups in 2019. On the contrary, the giants move frequently. At the Shanghai Auto Show in April 2019, Huawei’s self-driving cloud service Octopus was launched, including a simulation test platform. In December 2019, Waymo acquired LatentLogic to strengthen its simulation technology. In April 2020, Alibaba Dharma Academy released a “hybrid simulation test platform” for autonomous driving. GAC believes that: in the past, the virtual simulation platform was a supplementary role of the real vehicle test platform. In the L3 and above autonomous driving stage, virtual simulation testing has become an indispensable part of the autonomous driving research and development process. At present, GAC’s virtual simulation test accounts for more than 60% of the autonomous driving research and development process, and it will increase to 80% in the future. Simulation is not only indispensable in the research and development of bicycle intelligence, but also in the research and development of autonomous driving of vehicle-road collaborative routes. With the development of autonomous driving from “single-vehicle intelligence” to “vehicle-road coordination”, autonomous driving simulation also develops from dynamics simulation, sensor simulation, road simulation (static), to traffic flow simulation (dynamic) and smart city simulation. 51VR, which raised hundreds of millions of dollars, changed its name to 51WORLD after experiencing the VR bubble, and began to deploy digital twin cities and autonomous driving simulation. In November 2019, 51WORLD signed a contract to settle in Liangjiang New District, Chongqing, and will focus on expanding digital twin city innovative application products and autonomous vehicle simulation products in Chongqing. In fact, the combination of VR and autonomous driving simulation is not a helpless transformation of 51WORLD. In fact, VR/AR is becoming more and more important in autonomous driving simulation. The technical means of virtual scene construction usually include building scenes based on modeling software, building scenes based on completed games, building scenes based on VR/AR methods, and generating scenes based on high-precision maps. In August 2019, rFpro launched an autonomous driving simulation training system based on VR scenarios. This training system has the following characteristics:

(1) A series of automatic driving simulation operations can be completed in this software.
(2) The rFpro driving simulation system supports the import of third-party map models in various formats, including IPG ROAD5, .max, .fbx, OpenFlight, Open Scene Graph, and .obj with HIDEF high fidelity and other characteristics.

Due to the importance of autonomous driving simulation, the formulation of simulation standards has also been launched. ASAM (German Association for Automation and Measurement Systems Standards) is the leader of the global automated driving scenario simulation test standards (mainly OpenX series). Since ASAM launched the OpenX series of format standards, more than 100 companies around the world have participated in the formulation of this series of standards, including major OEMs in Europe, America and Japan, and Tier1. In the field of ASAM simulation verification, the OpenX series of standards mainly include five major sections: Open-DRIVE, OpenSCENARIO, Open Simulation Interface (OSI), Open-LABEL and OpenCRG. OpenDRIVE and OpenSCENARIO unify different data formats for simulation scenarios; OpenLABEL will provide a unified calibration method for raw data and scenarios; OSI connects automatic driving functions and simulation tools, and integrates a variety of sensors; OpenCRG implements road physics Interaction of information with static road scenes.

In September 2019, CATARC and ASAM jointly established the C-ASAM working group. Early member companies of the C-ASAM standard-setting working group include Huawei, SAIC, China Automotive Center Data Resource Center, Tencent, 51VR, Baidu, etc.

2019-2020 Autonomous Driving Simulation Industry Chain Research (Part 2) Table of Contents

Chapter two

Autonomous Driving Simulation Platform and Company Supplements

2.16 Ali Dharma Academy

2.16.1 Introduction of Ali Dharma Academy

2.16.2 Alibaba Autonomous Driving Technology Route

2.16.3 Alibaba AutoDrive Platform

2.16.4 Alibaba Autonomous Driving Simulation Platform

2.17 Saimo Technology

2.17.1 Introduction of Seimu Technology

2.17.2 Simul Technology Simulation Test Platform

2.17.3 External Cooperation of Seimu Technology

2.18 Huawei

2.18.1 Introduction of Huawei

2.18.2 Huawei Autonomous Driving Simulation Platform

2.18.3 Application of Huawei Emulation Platform

Chapter Four

Simulation Research on Road Weather Environment and Traffic Scenarios

4.1.1 Introduction to the construction of virtual scenes (weather, roads, traffic, etc.)

4.1.2 Road environment simulation

4.1.3 Weather Environment Simulation

4.1.4 Traffic flow simulation

4.1.5 Road, Weather, Traffic Scenario Simulation Company Overview

4.2 ESI Pro-SiVIC

4.2.1 ESI Company Profile

4.2.2 Products of ESI Company

4.2.3 Acquisition and Integration of ESI Group

4.2.4 Introduction to ESI Pro-SiVIC

4.2.5 ESI Pro-SiVIC Simulation Platform

4.2.6 Application of ESI Pro-SiVIC

4.2.7 The main operation flow of ESI Pro-SiVIC

4.2.8 Technical Capability of Pro-SiVIC

4.3 rFpro

4.3.1 rFpro Company Profile

4.3.2 rFpro Autonomous Driving Simulation Platform

4.3.3 Simulation test process and platform advantages of rFpro

4.3.4 rFpro Autopilot Test in VR

4.3.5 Partners of rFpro

4.3.6 Application of rFpro

4.4 Cognata

4.4.1 Introduction to Cognata

4.4.2 Introduction to Cognata Simulation Platform

4.4.3 Process and Features of Cognata Autonomous Driving Simulation

4.4.4 Cognata Partners

4.5 Parallel Domain

4.5.1 Introduction to Parrallel Domain

4.5.2 Parrallel Domain Simulation Platform

4.5.3 Advantages of Parallel Domain Simulation Platform

4.5.4 Application of Parallel Domain Simulation Platform

4.6 Metamoto

4.6.1 Introduction to Metamoto

4.6.2 Introduction of Metamoto Simulation Platform

4.6.3 Metamoto simulation platform editing

4.6.4 Metamoto simulation platform operation

4.6.5 Analysis of Metamoto Simulation Platform

4.6.6 Metamoto’s external cooperation

4.7 AAI

4.7.1 Introduction to AAI

4.7.2 AAI Main Products & Solutions

4.7.3 AAI Application

4.7.4 AAI cooperates with XXX

4.8 Applied Intuition

4.8.1 Introduction to Applied Intuition

4.8.2 Applied Intuition Simulation Platform

4.8.3 Applied Intuition Application Case 1

4.8.4 Applied Intuition Application Case 2

4.8.5 Applied Intuition Application Case 3

4.9 Ascent

4.9.1 Introduction to Ascent

4.9.2 Ascent Simulator Platform

4.10 Ansible Motion

4.10.1 Introduction to Ansible Motion

4.10.2 Main Products of Ansible Motion

4.10.3 Ansible Motion Solutions

4.11 UNITY

4.11.1 Introduction to UNITY

4.11.2 UNITY Autonomous Driving Simulation Solution

4.11.3 External Cooperation of Unity

4.12 Other Scenario Simulation Software/Simulator

4.12.1 SUMO


4.12.3 RoadRunner

4.12.4 XXXX

4.12.5 XXXX

4.12.6 XXXX

4.12.7 XXXX


chapter Five

Sensor Simulation Research

5.1 Introduction to Sensor Simulation

5.1.1 Sensor Simulation – Introduction to LiDAR Simulation

5.1.2 Sensor Simulation – Lidar Simulation Parameter Configuration

5.1.3 Sensor Simulation – Introduction to Camera Simulation (1)

5.1.4 Sensor Simulation – Introduction to Camera Simulation (2)

5.1.5 Sensor Simulation – Introduction to Millimeter Wave Radar Simulation

5.1.6 Sensor Simulation – Introduction to Millimeter Wave Radar Simulation (2)

5.1.7 Sensor Simulation – Other Sensor Simulation

5.1.8 Sensor Simulation Company Profile

5.2 MonoDrive

5.2.1 Introduction to MonoDrive

5.2.2 MonoDrive Sensor Simulator

5.2.3 MonoDrive Product Workflow

5.3 RightHook

5.3.1 Introduction to RightHook

5.3.2 Introduction to RightHook Simulation

5.3.3 Workflow of RightHook Simulation

5.3.4 RightHook Solution


5.4.1 OPTIS Company Profile

5.4.2 Introduction of Main Products of OPTIS

5.4.3 Application of main OPTIS products

5.4.4 Customers and Partners of OPTIS

5.5 Claytex

Chapter Six

Research on Simulation Interface

6.1.1 Introduction to Simulation System Interface

6.1.2 Classification of Simulation System Interfaces

6.1.3 Introduction to Hardware-in-the-Loop Simulation

6.1.4 Hardware-in-the-Loop Simulation Company Profile

6.2 NI

6.2.1 Introduction to NI

6.2.2 NI Industry Applications

6.2.3 VRTS

6.2.4 HIL system

6.2.5 Camera and V2X HIL testing

6.2.6 ADAS Sensor Fusion HIL Test Solution

6.3 ETAS

6.3.1 Introduction to ETAS

6.3.2 COSYM

6.3.3 LABCAR system components

6.3.4 LABCAR software products

6.3.5 LABCAR Simulation Model

6.3.6 LABCAR Simulation Model Products

6.4 Vector

6.4.1 Introduction to Vector

6.4.2 Introduction to DYNA4

6.4.3 DYNA4 function

6.4.4 DYNA4 Application

6.4.5 Simulation interface

6.5 dSPACE

6.5.1 Introduction to dSPACE

6.5.2 Introduction to dSPACE Real-Time Simulation System

6.5.3 dSPACE Development High-Performance Simulation Environment

6.5.4 dSPACE real-time simulation system solution


6.5.6 Test V2N/V2Cloud Application

6.5.7 dSPACE Simulation Toolchain

6.5.8 dSPACE Simulation Interface Software

6.5.9 Uhnder’s Automotive Radar Target Simulator Using dSPACE

6.5.10 dSPACE Partners

Chapter VII

Standardization and future trends

7.1 International Organization for the Standardization of Autonomous Driving Simulation

7.1.1 Introduction to ASAM

7.1.2 The OpenX Family of Standards for ASAM

7.1.3 C-ASAM Working Group

7.1.4 IAMTS

7.2 Status Quo of Autonomous Driving Simulation Testing Standards in China

7.2.1 National road test standards for autonomous driving (1)

7.2.2 National road test standards for autonomous driving (2)

7.2.3 Provincial and Municipal Autonomous Driving Road Test Standards (1)

7.2.4 Provincial and Municipal Autonomous Driving Road Test Standards (2)

7.3 Status Quo of China’s Participation in International Standards

7.3.1 China actively participates in international standards

7.3.2 Participate in the formulation of international standards for autonomous driving test scenarios

7.4 Future Development Trend

7.5 Autonomous Driving Simulation Layout of OEMs

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