How Data Labeling Services Empower Self-Driving Industry 2021? — Part5
On May 31, 2021, it is shown that didi auto-driving completed a new round of strategic financing with an amount of over US $300 million, and the investor was GAC group.
Since its split in 2019, Didi has raised more than the US $1.1 billion in total.
In 2016, Didi began to develop and test automatic driving.
In August 2019, Didi upgraded its automatic driving department to the company. On May 29, 2020, Didi travel announced that its autonomous driving company had completed the first round of financing of over US $500 million, which was led by Softbank vision fund phase 2.
On January 28, 2021, Didi autopilot completed a $300 million financing. This round is led by IDG capital, followed by CPE, Paulson, Sino Russian investment fund, Guotai Junan International, CCB International, and other investment institutions.
It is said that after the new round of financing, the valuation of didi automatic driving will exceed that of Pony AI. Pony AI is one of the most powerful auto-driving start-ups in the industry in China. It is shown that, up to February 2021, Pony AI has raised a total of over US $1.1 billion, with a valuation of over US $5.3 billion.
In June 2020, Didi automatic driving announced to carry out the manned test in Shanghai. At present, it has obtained road test licenses in Shanghai, Beijing, Suzhou, Hefei, California, etc. According to Didi, its autopilot R & D department currently has more than 500 people, covering road condition perception, high-precision map, behavior prediction, planning and control, infrastructure and simulation, cloud control, and Internet of Vehicles, autopilot scheduling products, and operation teams.
Mr.Cheng, CEO of didi travel, said that by 2025, more than 1 million units of shared cars are expected to be popularized on the didi platform, and the iterative version of new models will be able to carry Didi’s self-developed driverless module. By 2030, Didi’s customized cars hope to be fully autonomous.
According to the official report, there are nearly 5200 autopilot-related enterprises in China. Among them, more than 30% of the registered capital is more than 10 million, and nearly 90% of them are limited liability companies.
From the perspective of industry distribution, automatic driving-related enterprises are mostly distributed in the wholesale and retail industries, with more than 1500 enterprises, accounting for 31%, followed by scientific research and technical services, information transmission, software, and information technology services, accounting for 29% and 20% respectively.
In terms of geographical distribution, Guangdong has the largest number of autonomous driving-related enterprises, with more than 1600 enterprises, accounting for 32%. In addition, Hebei, Jiangsu, and Shandong province have more than 300 autonomous driving-related enterprises.
Overall, in the past decade, the annual registration volume of autonomous driving-related enterprises showed an overall upward trend, with an annual growth rate of more than 17%, and the total registration volume increased rapidly. Among them, nearly 1200 new related enterprises were added on the list in China in 2019, which is the year with the highest annual registration volume. In 2020, nearly 900 new related enterprises in China were up. Up to May 27, 2021, China has added more than 300 related enterprises this year.
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Self-driving technology is going to transform the transportation industry, social and daily lives. It’s hard to know when that day will arrive. As life is priceless, we have to seek perfection from the beginning.
It’s challenging for self-driving manufacturers to internally meet the burgeoning demand for high-quality data annotation.
The labeler used to annotate point by point, which cost lots of time.
3D annotation and video annotation are considered as the toughest services in data labeling. At present, object tracking algorithms based on machine learning have already assisted video annotation. The annotator annotates the objects on the first frame, and then the algorithm tracks the ones in the subsequent frames. The annotator only needs to adjust the annotation when the algorithm doesn’t function well. It is 100 times faster than before.
Thanks to an AI-assisted system, the corresponding parts can be automatic can be transcribed and the human only needs to check and modify the wrong parts.
Nowadays, some AI-assisted tools come to practice, standing out in 2 factors.
- Cost reducing: With the help of AI-assisted capabilities, clients can save more money as the labor cost goes down.
- Time reducing: Make the large-scale requirement of training data done in a short time. Using AI-assisted tool can improve efficiency multiple times
Can we get rid of the human workforce?
The answer is no.
In fact, manually labeled data is less prone to errors regarding quality assurance and data exceptions.
The human workforce cannot be totally replaced by some tools leading with an AI-based automation feature, especially dealing with exception, edge cases, complex data labeling scenarios, etc.
As mentioned, data accuracy is vital in the car industry, here comes another question: Should I build up an in-house team?
Before making the final decision, you have to keep 2 points in mind:
1 Complex process: including annotation tools and data pre-processing built-up, labeler performance training and following, data validation and quality check, etc.
2 High financial involvement: such as infrastructure labor cost, R&D, etc.
Compared to in-house infrastructure, outsourcing service needs effective communication and fast feedback. It is very important for manufacturers to choose the right one who can serve as “an extension department” of their company.
The Following Parts Should Also be Taken Into Account:
Progress preview: clients can monitor the labeling progress in real-time on the dashboard
Result preview: clients can get the results in real-time on the dashboard
Customer service: clients can communicate with data workers about the changes so that workers can respond quickly and make changes in workflow
In conclusion, in the Autocar industry, we rely much on the human workforce. Therefore, in terms of outsourcing partner choosing, we have to make sure of the flexible engagement in the labeling loop as we need labelers to respond quickly and make changes in workflow, based on the model testing and validation phase.
ByteBridge is a human-powered and ML-powered data labeling tooling platform. We provide scalable, high-quality training data for ML/AI industry with flexible workflow.
Quality and Accuracy
- ML-assisted capacity can help reduce human errors by automatically pre-labeling
- The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
- Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
- All work results are completely screened and inspected by the machine and human workforce.
In this way, ByteBridge can affirm our data acceptance and accuracy rate is over 98%
- The client can set labeling rules directly on the dashboard.
- The client can iterate data features, attributes, and task flow, scale up or down, make changes based on what they are learning about the model’s performance in each step of test and validation
For example, you can choose an Autopilot Annotation Template for your project:
- Progress preview: client can monitor the labeling progress in real-time on the dashboard
- Result preview: client can get the results in real-time on the dashboard
Real-time Outputs: client can get real-time output results through API. We support JSON, XML, CSV, etc., and we can provide customizable datatype to meet your needs.
These labeling tools are already available on the dashboard: Image Classification, 2D Boxing, Polygon, Cuboid.
3D Point Cloud Annotation Service
ByteBridge self-developed 3D Point Cloud labeling, quality inspection tool, and pre-labeling functions can complete high-quality and high-precision 3D point cloud annotation for 2D-3D fusion or 3D images provided by different manufacturers and equipment, and provide one-station management service of labeling, QA, and QC.
More info: ByteBridge Launches World’s First Mobile 3D Point Cloud Data Labeling Service
3D Point Cloud Annotation Types:
- Sensor Fusion Cuboids: 12 categories include car, truck, heavy vehicle, two-wheeled vehicle, pedestrian, etc.
- Sensor Fusion Segmentation: obstacles classification, different types of lanes differentiation
- Sensor Fusion Cuboids Tracking
① Tracking the same object with the same ID, labeling the leaving state;
② Point clouds or time-aligned images could be provided, point clouds outputs only.
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
As the quality of the labeled dataset determines the success of the self-driving industry, cooperation with a reliable partner can help developers to overcome the data labeling challenges.
We can provide personalized annotation tools and services according to customer requirements.
If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.
Please feel free to contact us: firstname.lastname@example.org
1 Introduction of Giant Self-driving Companies: Tesla and BAIC
2 Introduction of Giant Self-driving Company Waymo
3 The World’s First Driverless Law to Allow L4 Class Automatic Driving on the Road in Germany
4 Is Lidar the Future of the Self-Driving Industry2021?
5 How Auto-Driving Achieved through Machine Learning?