DUBLIN--(BUSINESS WIRE)--The "China Autonomous Driving Data Closed Loop Research Report, 2022" report has been added to ResearchAndMarkets.com's offering.

The development of autonomous driving is gradually driven by data rather than technology

Today, autonomous driving sensor solutions and computing platforms have become increasingly homogeneous, and the technology gap between suppliers is narrowing. In the past two years, the iteration of autonomous driving technology has advanced rapidly, and mass production has accelerated.

A total of 4.79 million passenger cars with L2 assisted driving were insured in China in 2021, a year-on-year increase of 58.0%. From January to June 2022, the penetration rate of L2 assisted driving in the Chinese new passenger car market climbed to 32.4%.

For autonomous driving, data runs through the entire life cycle ranging from R&D, testing, mass production, operation to maintenance. As the number of sensors in intelligent connected vehicles swells, the amount of data generated by ADAS and autonomous vehicles is growing exponentially, from gigabytes to terabytes, petabytes, exabytes, and even zettabytes in the future. The evolution of data-driven vehicles can meet the personalized demand of users, and facilitate the long-term development of automakers.

Data closed loop becomes the core of iterative upgrade of autonomous driving

The premise of continuous iteration of automatic driving systems lies in constant optimization of algorithms which hinges on the efficiency of data closed loop systems. The efficient flow of data in each scenario of autonomous driving development is crucial, and data intelligence will become the key to accelerating mass production of autonomous vehicles.

In December 2021, Haomo.AI officially released MANA (Snow Lake), the first autonomous driving data intelligence system in China, to accelerate evolution of autonomous driving technology from the perspectives of perception, cognition, annotation, simulation and calculation. In the next three years, the assisted driving system of Haomo.AI will land on more than 1 million passenger cars.

By virtue of its fully self-developed autonomous driving system, Haomo.AI has achieved remarkable advantages in data accumulation, processing and application. Massive data brings about technological iterative advantages, like obvious cost reduction and efficiency improvement.

Data collection/cleaning

The massive unstructured data (images, video, speech) collected by automotive cameras, radar, LiDAR, and ultrasonic radar can be raw and messy. To make them meaningful, they should be cleaned, structured, and organized. At first, the data from multiple sources should be imported into appropriate repositories with their formats being standardized and they should be aggregated according to relevant rules.

Data annotation

The structured data that are cleaned after data collection should be labeled. Labeling is the process of assigning encoded values to raw data. Encoded values include, but are not limited to, assigning class labels, drawing bounding boxes, and marking object boundaries. High-quality annotation is needed to teach supervised learning models what objects are and to measure the performance of trained models.

In the field of autonomous driving, data annotation usually covers scenarios where vehicles are changing lanes to overtake, passing through intersections, turning left or right without traffic light control, running red lights and parking on roadsides illegally, pedestrians are jaywalking, etc.

Popular annotation tools are involved with general picture frames, lane line annotation, driver face annotation, 3D point cloud annotation, 2D/3D fusion annotation, panoramic semantic segmentation, etc. Prompted by development of big data and the spike in the number of large datasets, data annotation tools are used more and more widely.

Data transmission

Nowadays, data collection occurs every few milliseconds, requiring high-precision data in thousands of signal dimensions (such as bus signals, the internal state of sensors, software embedment, user behaviors, and environmental perception data, etc.). At the same time, in order to avoid data loss, disorder, hopping and delay, the transmission/storage cost is greatly reduced under the premise of high precision and high quality. The long uplink and downlink (from automotive MCU, DCU, gateways, 4G/5G to the cloud) of IoV data require the data transmission quality of each link node.

Data storage

In order to perceive the surrounding environment more clearly, autonomous vehicles carry more sensors and generate massive data. Some high-level autonomous driving systems are even equipped with more than 40 assorted sensors to accurately perceive 360 environment around vehicles. The R&D of autonomous driving systems has to go through multiple links such as data collection, data aggregation, cleaning and marking, model training, simulation, big data analysis, etc..

Key Topics Covered:

1 Introduction to Autonomous Driving Data Industry Chain

1.1 Overview of Automotive Data and Autonomous Driving Data

1.1.1 Classification of Automotive Data

1.1.2 China's Laws and Regulations for Automotive Data Security

1.1.3 Data Volume and Computing Power Requirements by Autonomous Driving Level

1.1.4 Computing Power of Assisted Driving of Some New Vehicles on the Market

1.1.5 Basic Requirements for Data Storage of Autonomous Vehicles

1.1.6 The Efficient Development of Autonomous Driving Requires Construction of a Data Closed Loop System

1.1.7 Workflow of Conventional Data Closed Loop

1.1.8 Workflow of AI Data Closed Loop

1.2 Data Acquisition

1.2.1 Status Quo

1.2.2 Value

1.2.3 Acquisition Methods of Traditional Structured Data

1.2.4 Unstructured Data

1.2.5 Problems of Data Acquisition in Corner Cases

1.3 Data Annotation

1.3.1 Definition

1.3.2 Industry Chain and Ecology

1.3.3 Autonomous Driving Data Annotation

1.3.4 Types of Autonomous Driving Data Annotation

1.3.5 More Data Required by Model Training

1.3.6 Harder and More Demanding 3D Annotation

1.3.7 L3+ Requires Massive High-quality Data

1.4 Shadow Mode

1.4.1 Definition

1.4.2 Accumulated Mileage of Tesla Autopilot

1.4.3 Application Examples of Shadow Mode of Some Enterprises

1.5 Overview of Autonomous Driving Data Industry Chain

2 Typical Data Acquisition and Annotation Companies

2.1 Testin

2.2 MindFlow

2.3 Appen

2.4 Graviti

2.5 Jinglianwen Technology

2.6 Speechocean

3 Data Closed Loop Solution Providers

3.1 Kunyi Electronics

3.2 EXCEEDDATA

3.3 Baidu

3.4 VNET

3.5 Momenta

3.6 CalmCar

3.7 Molar Intelligence

3.8 SandStone

3.9 Amazon

4 Data Closed Loop Layout of Main Tier1/Tier2 Suppliers

4.1 Hong Jing Drive

4.2 Pony.ai

4.3 Freetech

4.4 NavInfo

4.5 Idriverplus

4.6 iMotion

4.7 Haomo.AI

4.8 UISEE

4.9 Other Tier1/Tier2 Suppliers

4.9.1 MINIEYE

4.9.2 Heading Data

4.9.3 Leadgentech

4.9.4 HoloMatic Technology

4.9.5 Joyson Electronics

4.9.6 Neusoft Reach

4.9.7 MOGO

4.9.8 juefx.com

4.9.9 QCraft

4.9.10 AutoX

5 Data Closed Loop Layout of Other Companies

5.1 Chip Vendors

5.2 Tesla

5.3 DeepWay

For more information about this report visit https://www.researchandmarkets.com/r/xin32v

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