China Autonomous Driving Data Closed Loop Research Report 2023: When Constructing a Data Closed Loop Ecosystem, Companies Quicken their Pace of Building ‘Digital Intelligence/Database’ Capabilities – ResearchAndMarkets.com

DUBLIN–(BUSINESS WIRE)–The “China Autonomous Driving Data Closed Loop Research Report, 2023” report has been added to ResearchAndMarkets.com’s offering.

In the stage of Autonomous Driving 3.0, work hard on end-to-end development to control data.

At present, autonomous driving has entered the stage 3.0. Differing from the software-driven stage 2.0 based on artificial rules, in the stage 3.0, the iteration of autonomous driving functions is driven by big data and foundation models; the perception mode is that multimodal sensors jointly output the results, while the information fusion mode evolves from post-fusion to in-fusion and pre-fusion.

As concerns data capability building, companies have shifted their focus to the efficiency and cost of the data closed loop, and can cope with more Corner Cases (extreme cases, e.g., traffic accidents, severe weathers or complex road conditions) on the premise of data compliance and data security, so as to expedite the development of user experience from ‘fatigue relief’ to ‘scenario-based comfortable experience’. Wherein, OEMs concentrate their efforts on improving application of autonomous driving in such scenario as city areas, urban overpasses and highways.

In highway scenarios, users can securely enable autonomous driving functions. The autonomous driving system provides a guide for accurate and comfortable on-ramp and off-ramp, keeps vehicles centering in lanes, allows vehicles to come around sharp corners at high speeds, balances follow and stop on congested road sections, intelligently selects the best lane, horizontally avoids and overtakes slow vehicles in lanes, and intelligently recognizes and avoids accidents and slow vehicles.

In urban scenarios, city NOA functions like NIO NOP+, Huawei NCA, and Xpeng XNGP have come into service, with the improving scenario generalization capabilities for vehicle functions. For example, Huawei’s intelligent driving system ADS2.0 mounted on Avatr allows the car to successfully pass through between the two trucks on a narrow urban street, and rivals or even outperforms humans in making decisions.

In short, the autonomous driving system performs ever better in automatic lane change, obstacle avoidance and other scenarios, and lets drivers take control of vehicles less frequently. Behind the vehicle intelligence competition is the efficient flow of data in the vehicle-cloud closed loop. Examples include Baidu AI Cloud’s autonomous driving data closed loop solution that provides full-cycle autonomous driving data operation services and an autonomous driving tool chain platform to solve problems in the process of data collection, processing, and use.

The stage of data-driven Autonomous Driving 3.0 starts, and companies double down on building or improving all the links of their data closed loop system.

The essence of the Autonomous Driving 3.0 is to be driven by data for ever higher data mining efficiency and date utilization. In this stage, the scale of vehicle test data can cover more than 100 million kilometers, which poses challenges to the collection, annotation and other links in the data closed loop. In this regard, automakers raise the efficiency of data processing by way of improving the flexible collection logic of the shadow mode and simulating zero prototypes to gear up for algorithm iteration, model training and deployment.

When constructing a data closed loop ecosystem, companies quicken their pace of building ‘digital intelligence/data base’ capabilities.

Data, algorithm, and computing power are the three cornerstones of autonomous driving technology. The volume and quality of data determine the upper limit of algorithm capabilities, and computing power is the carrier of data processing. The integration of software and hardware means the ‘smoothness’ of adaptability between algorithms and domain controller/chip. Currently just a few companies like Tesla in the industry have built a complete intelligent ecosystem of ‘data + algorithm + computing power’, and take 100% control of data. To have control over data, OEMs and software algorithm companies are running after them.

Build intelligent computing centers

As the initial investment in supercomputing centers is relatively high, OEMs and Tier 1 suppliers generally budget over RMB100 million for building AI computing centers. In the case of Dojo, Tesla’s supercomputer platform that was put into use in July 2023, Tesla will invest more than USD1 billion in Dojo in 2024 to facilitate training of the supercomputer and neural networks.

Even so, automakers or technology providers with a long-term plan for autonomous driving are building their own supercomputing centers to hold stable computing resources and shorten the development cycle and the time-to-market of autonomous driving products. One example is ‘Fuyao’, Xpeng’s autonomous driving AI intelligent computing center co-funded with Alibaba in August 2022. The center can speed up the model training of autonomous driving algorithms by 170 times, and has the scope to increase computing power by 10 to 100 times in the future.

‘Digital intelligence/data base’ capability building

The full life cycle of intelligent vehicles needs to be driven by data, and it is the data-based vehicle-cloud full-link capability base that some autonomous driving solution providers work hard to build. For example, ExceedData’s vehicle-cloud integrated computing architecture combines with its vehicle high-performance time-series database to build a data base for intelligent vehicles, and redefines the cost and efficiency of vehicle data intelligence, enabling an 85% reduction in the total cost. The data base solution has been well accepted by first-tier automakers including FAW, SAIC, SAIC Z-ONE, Human Horizons, Dongfeng Voyah, BAIC and Geely, and has been mass-produced and designated for more than 10 vehicle models.

BEV+Transformer enables end-to-end perception and decision integration.

The enhancement of autonomous driving capabilities is the result of the joint optimization of “more data + better algorithms + higher computing power”, and it is also the inevitable result of advancement of perception, decision, planning and control technologies.

A variety of complex scenarios, especially corner cases, require higher perception and decision capabilities of autonomous driving. BEV technology enhances the perception of autonomous driving systems by providing a holistic perspective. Transformer models can be used to extract features from multimodal data, such as LiDAR point clouds, images and radar data. The end-to-end training on these data allows Transformer to automatically learn the internal structure and interrelation of these data, so as to effectively recognize and locate obstacles in environments.

BEV+Transformer can build an end-to-end autonomous driving system to achieve high-precision perception, prediction and decision. In SenseTime’s case, based on a multimodal foundation model, it can enable data closed loops of prediction and decision. UniAD, SenseTime’s perception-decision integrated end-to-end autonomous driving solution, increases the lane line prediction accuracy by 30%, and reduces the errors of motion displacement prediction by nearly 40% and the planning errors by nearly 30%. With regard to AI decision, SenseTime and Shanghai Artificial Intelligence Laboratory launched OpenDILab, a decision AI platform which can also be used in autonomous driving for planning and control.

Key Topics Covered:

1 Overview of Autonomous Driving Data Closed Loop

1.1 Three Cornerstones for Development of Autonomous Driving Technology

1.2 What Is Data Closed Loop

2 Application of Foundation Model in Autonomous Driving Data Closed Loop

2.1 Foundation Model Based on Neural Network

2.2 Post-fusion, Feature-level Fusion and Pre-fusion

2.3 BEV+Transformer Has Become the Mainstream Solution at Present

2.4 Overview of Foundation Models Enabling Autonomous Driving

2.5 Foundation Model Capability Building and Tool Chain

2.6 Overview of Foundation Model Layout in Automotive Industry

2.7 AI Foundation Model Accelerates Automotive GPT

2.8 AI Foundation Model Accelerates Implementation of City NOA

3 Application of Cloud Platform in Autonomous Driving Data Closed Loop

3.1 Roles of Cloud Platform in Data Closed Loop (1)

3.2 Roles of Cloud Platform in Data Closed Loop (2)

3.3 Roles of Cloud Platform in Data Closed Loop (3)

3.4 List of Autonomous Driving Cloud Supercomputing Centers in China

3.5 Typical: AWS Cloud Platform Data Closed Loop

3.6 Typical: Volcengine Data Closed Loop Cloud Service Platform

3.7 Typical: Alibaba Cloud Data Closed Loop

4 Typical Simulation Companies in Autonomous Driving Data Closed Loop

4.1 The International Organization for the Standardization of Autonomous Driving Simulation

4.2 The Localization of ASAM Standards in China

4.3 Domains of ASAM Standards

4.4 Foreign Vehicle Dynamics Benchmarking Companies

4.5 Foreign Traffic Scene Simulation Benchmarking Companies

4.6 Foreign Virtual Scene Simulation Benchmarking Companies

4.7 Foreign Sensor Simulation Benchmarking Companies

4.8 Foreign Hardware-in-the-Loop Simulation Benchmarking Companies

4.9 Dynamics of Autonomous Driving Simulation Platforms in China

4.10 Typical Company: IAE

4.11 Typical Company: PanoSim

4.12 Typical Company: 51WORLD

4.13 Typical Company: Cognata

4.14 Typical Company: VI-grade

5 Data Closed Loop Layout of Typical OEMs

5.1 BYD

5.2 SAIC

5.3 Changan Automobile

5.4 Geely

5.5 Xpeng

5.6 Li Auto

5.7 Tesla

6 Data Closed Loop Layout of Typical Autonomous Driving Providers

6.1 Baidu

6.2 Huawei

6.3 Freetech

6.4 MAXIEYE

6.5 Nullmax

6.6 Pony.ai

6.7 Momenta

7 Typical Data Closed Loop Solution Providers

7.1 Haomo.ai

7.2 SenseTime

7.3 EXCEEDDATA

7.4 LiangDao Intelligence

7.5 JueFX Technology

7.6 Rhino

7.7 Horizon Robotics

7.8 Black Sesame Technologies

7.9 Kunyi Electronics

For more information about this report visit https://www.researchandmarkets.com/r/vt6i4p

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