The "Autonomous Driving Simulation Industry Report, 2024" report has been added to ResearchAndMarkets.com's offering.

Autonomous Driving Simulation Research: Three Trends of Simulation Favoring the Implementation of High-level Intelligent Driving.

On November 17, 2023, the Ministry of Industry and Information Technology and other three ministries issued the Notice on Piloting Access and On-road Passage of Intelligent Connected Vehicles. Up to now, many OEMs including BYD, BMW, IM, Mercedes-Benz, Deepal, Avatr, ARCFOX, AITO, Jiyue and GAC Aion have obtained highway or urban L3 autonomous driving test licenses. At present, the application of high-level intelligent driving functions represented by urban NOA is being accelerated, and L3 and above autonomous driving systems should be safe and robust enough to deal with countless edge/long tail cases in urban areas.

The commercialization of L3 intelligent driving systems needs more than one billion kilometers of test mileage, and actual road tests are costly and time-consuming, with low use case coverage. However, simulation tests can quickly solve this problem in a short time and at low cost. In Xpeng's case, in addition to the roadside data provided by car owners every day, Xpeng is working to build extreme scenarios in virtual space combining simulation for the intelligent driving system to learn and understand. By the end of 2023, the simulation mileage of Xpeng had reached 122 million kilometers.

In the 'three-pillar' test approach for intelligent driving, in simulation tests, different traffic scenes, road conditions, weather illumination and abnormalities are simulated through the virtual environment to evaluate the functions, response and decision capabilities of the autonomous driving systems in various circumstances.

In the above figure, the autonomous driving simulation platform should support traffic scene simulation (static scene restoration and dynamic scene simulation), environment-aware sensor simulation (modeling and simulation of sensors such as camera, LiDAR, radar and GPS/IMU), vehicle dynamics simulation, etc., so as to verify simulation tests ranging from perception to control. According to tested objects, the autonomous driving simulation platform enables in-the-loop tests such as: model in the loop (MIL), software in the loop (SIL), hardware in the loop (HIL), driver in the loop (DIL) and vehicle in the loop (VIL). At present, simulation test companies vary in capabilities, as shown in the table below.

Autonomous driving simulation tests have entered the precise simulation stage with high fidelity and high reduction.

In the whole link of "perception-prediction-decision-planning-control", perception corresponds to all kinds of sensors which collect the external environment information of the vehicle, such as traffic flow, road conditions, weather, illumination and abnormalities. It mainly involves camera simulation, LiDAR simulation, radar simulation, and positioning simulation (GPS, IMU).

At present, many companies are working on fine-grained simulation engineering practices, for example, high fidelity simulation of real road environment, dynamic traffic scene and vehicle/pedestrian behavior, and accurate restoration of detailed physical phenomena and dynamic sensor performance, so as to quickly verify the performance of autonomous driving systems and provide comprehensive test and verification reports.

In addition, multi-sensor concurrent simulation tests greatly improve the R&D and testing efficiency of perception algorithms. In terms of engineering practice, in May 2023 51Sim and VCARSYSTEM cooperated to successfully fulfill the closed loop of domain controllers from SIL to HIL in China's autonomous driving tests, and realized the thorough localization of domain controller in-the-loop simulation tests. In this domestic domain controller in-the-loop solution based on Journey 5, the intelligent driving data reinjection system independently developed by VCARSYSTEM supports simultaneous injection of sensor data from multiple high-definition cameras, LiDAR, radar, ultrasonic radar, GNSS&IMU and so on, and easily reproduces specific scenes and environments via 51Sim-One, an autonomous driving simulation test platform, thereby greatly improving the R&D and testing efficiency of perception algorithms.

Automatic generation and scene generalization are essential.

At present, how to build a corner case scene is a big challenge for the industry. It is the significance of simulation tests to reproduce scenes such as high-risk working conditions, extreme weather conditions, complex traffic environment and edge events, which are difficult to cover in actual road tests. Especially for large-scale tests of safety-critical scenes, automated simulation technology based on AI technology is needed to cover more scenarios.

Coverage-based quality is a more detailed and comprehensive autonomous driving safety test method. It focuses on the quality of test coverage, that is, whether the system has experienced various possible situations and scenarios. By defining a range of test cases and test scenes, this method can ensure that autonomous driving systems can be tested in various road conditions, traffic conditions and abnormalities. The quality of coverage can involve changes in road conditions, traffic behaviors, special weather conditions, emergencies and more.

Moveover, AI technology and large language models are gradually integrated into simulation tests, playing an increasingly important role in automatic scene generation, automatic annotation, accelerating the construction of scene libraries, reducing the cost of simulation tests, lowering the threshold of simulation test technology and shortening the vehicle development cycle.

In the case of natural language interaction, 51WORLD's AIGC-Scenario Copilot supports fully natural language interaction. Without tedious manual editing and code, it only needs scene descriptions, for example, 'add an action, first change the lane to the right, and then slow down to 0'. By using an AI large language model, an autonomous driving simulation test scene conforming to the OpenSCENARIO standard can be generated, and an unknown dangerous scene can also be generated to expand the boundary of the simulation test.

In addition to simulation platforms and scene library generalization capabilities mentioned above, simulation evaluation systems for autonomous driving testing are also essential in autonomous driving technology commercialization. Simulation evaluation refers to the evaluation and optimization of all aspects of an autonomous driving system by means of simulation testing to ensure its safe, reliable and efficient operation on actual roads. Simulation evaluation mainly includes autonomous driving system evaluation and simulation test system evaluation, of which simulation test system evaluation includes the evaluation of scenario coverage, scene realness, scene effectiveness and simulation efficiency.

Capitalization and sharing of scene library data help to drag down costs and improve efficiency of high-level autonomous driving training and testing.

In simulation tests, in addition to automatic scene generation based on road data (dSPACE Autera, NI data collection solution, VI-Grade AutoHawk, etc.), an all-scenario synthetic data simulation material library can help developers keep training, testing and verifying autonomous driving systems in massive driving scenes, especially safety-critical scenes, to improve algorithm iteration efficiency and closed-loop test efficiency and depth.

Amid surging demand for training and test data of autonomous driving systems, it is difficult to collect diverse and high-quality long-tail scenes on a large scale, and filter the required scenes. In view of this, some simulation solution providers such as SYNKROTRON, IAE and 51Sim have begun to work on the data assetization of simulation scene libraries (including standard regulatory scenes, accident scenes, natural driving scenes, dangerous/extreme scenes, and reconstruction scenes). They have also made a positive response to the "Three-Year Action Plan for 'Data Elements x" (2024-2026), and assisted the intelligent connection industry with assetization of data, which have been traded on Shenzhen Data Exchange, Shanghai Data Exchange, Suzhou Big Data Exchange, and Northern Big Data Trading Center.

Key Topics Covered:

1 Overview of Autonomous Driving Simulation

1.1 Significance of Simulation Testing to Autonomous Driving R&D

1.2 Classification of Autonomous Driving Simulation Technology

1.3 Summary of Autonomous Driving Simulation Test Data Platform Solutions

1.4 Summary of Autonomous Driving Simulation Software

1.5 Comparison among Domestic Autonomous Driving Simulation Companies and OEMs

1.6 Comparison among Foreign Autonomous Driving Simulation Companies and OEMs

1.7 Autonomous Driving Simulation Industry Chain

2 Autonomous Driving Simulation Test Scene Libraries

2.1 International Organization for Standardization of Autonomous Driving Simulation: ASAM

2.2 autonomous driving Simulation Organization: ISO & Europe Pegasus Project

2.3 Chinese ASAM Standards: C-ASAM Working Group

2.4 Status Quo of Autonomous Driving Scene Simulation Standards and Regulations

2.5 Autonomous Driving Simulation Test Scene Libraries and Their Features

2.6 Simulation Assessment

3 Simulation Technology

3.1 The first National Digital Twin Standard

3.2 Practice of Digital Twin Ecosystem Construction

3.3 Typical Application Scenarios of Digital Twin

3.4 Digital Twin Application Case 1: Automobile Manufacturing

3.5 Digital Twin Application Case 2: Electric Drive Development - OEMs

3.6 Digital Twin Application Case 3: Cloudization of Cockpit Systems - OEMs

3.7 Digital Twin Application Case 4: Smart Expressways - XXX Simulation Solution Providers

3.8 AI and Simulation

3.8.1 Application of AI in Simulation Platforms

3.8.2 AI Simulation Application Cases: OEMs

3.8.3 What Changes Have AIGC and Foundation Models Brought to AD Simulation?

3.8.4 Simulation Cases Based on Foundation Model Training: OEMs

3.8.5 Cases of AIGC Automatically Generating Simulation Test Scenes: Simulation Solution Providers

4 Domestic Simulation Platform Solution Providers

4.1 PanoSim

4.2 PilotD

4.3 Vehinfo

4.4 Keymotek

4.5 Beijing Oriental Jicheng

4.6 SaimoAI

4.7 51WORLD

4.8 IAE

4.9 SYNKROTRON

4.10 RisenLighten

4.11 Dotrust Technologies

4.12 BeCreator

4.13 Tsing Standard

4.14 RACO

4.15 Jingwei Hirain

4.16 EASY SIMULATION SMART

4.17 ALINX

4.18 Kunyi Electronics

4.19 Tencent

4.20 Baidu

4.21 Huawei

5 Overseas Simulation Platform Solution Providers

5.1 Foretellix

5.2 dSPACE

5.3 NI

5.4 Vector

5.5 MathWorks

5.6 NVIDIA

5.7 IPG Automotive

5.8 Ansys (affiliated to Synopsys)

5.9 VI-Grade

5.10 Ansible Motion

5.11 Applied Intuition

5.12 Anyverse

6 Trends of Autonomous Driving Simulation Testing

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