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Why did Germany and Mercedes-Benz Give the Green Light for L3 Autonomous Driving Together? -Part3

Now back to the first question of our article, why is Germany able to set the stage?

Germany passed the new “Road Traffic Law” (SVG) on June 21, 2017, to open the L3 autonomous driving system as early as four years ago. At that time, Germany became the first to provide this system. The second is South Korea. Currently, China has no corresponding regulations.

However, car companies are not eager to announce mass-produced vehicles even though most of them have the capabilities in the field of L3 autonomous driving. The reason is that the legal responsibilities and ethical settings are too difficult for one company to promote.

Behind this step in Germany is the result of joint promotion by the regulatory authorities and car companies.

First, in 2016, the German Federal Government established an Ethics Committee to study legal and ethical issues in autonomous driving. In June 2017, the Ethics Committee passed a final report, which contained a total of 20 ethical rules, including the fact that humans safety is always the top priority.

The Ethics Committee also stipulated strict requirements for data protection, which today also become OEMs’main concern. Especially, Daimler internalized it in the development of its automation and autonomous systems. Daimler stated that three clear principles will always apply: transparency, self-determination, and data security.

Another problem is the international legal framework: different national laws and regulations influence automobiles’ sales and manufacturing standards. As a result, Daimler became the first company to advocate the establishment of an international uniform legal framework for autonomous driving.

“Progress must not stop at national boundaries. Legislation must keep up with technological progress. Otherwise, the first innovations in automation and autonomous driving will not be on the road.” Daimler Renata Jungo Brüngger said: “Legal certainty is a prerequisite for society to accept autonomous driving. For this reason, we will soon need international coordination of the legal framework.

At present, US states and EU member states currently implement different regulations. Several agreements around the world specify the legal framework of national road traffic legislation. The most important of these is the 1968 Vienna Convention on Road Traffic. However, the automated system had not been developed at that time.

Since the changes were made in March 2016, automated systems have been allowed. However, autonomous driving is not yet 100% possible, and the drivers still have to control vehicles according to the rules.

High-quality Autonomous Driving Training Data

In the current practice of artificial intelligence applications, different level of data quality demonstrates an obvious gap in the value of artificial intelligence solutions.

High-quality training data will maximize the efficiency of artificial intelligence, while low-quality AI data will be not only impossible to improve efficiency, but also will hinder the evolution of artificial intelligence to a certain extent.

Previously, the media reported that a user had a car accident while riding in a smart driving vehicle. After the investigation, it was discovered that the smart driving system failed to distinguish the difference between the white vehicle and the cloud and did not identify obstacles. The vehicle failed to brake in time, which in turn triggered tragic consequences.

In this case, the lack of accurate data on the distinction between white vehicles and the cloud is the direct factor leading to the tragedy.

Therefore, the measures to provide high-quality AI data for different scenarios and different needs have gradually become the consensus of artificial intelligence solutions.

Common Data Labeling Types Include:

A New Solution For the Self-Driving Data Annotation Project

ByteBridge, a human-powered and ML-powered data training platform provides high-quality services to collect and annotate different types of data such as text, image, audio, and video to accelerate the development of the machine learning industry.

Quality Guarantee

  • 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 machines and the human workforce

In this way, ByteBridge can affirm our data acceptance and accuracy rate is over 98%.

Communication Cost Saving

On ByteBridge’s SaaS dashboard, developers can start the labeling projects by using the labeling instruction template and get the results back instantly.
From online setting labeling briefing to expert support alongside, the instruction communication is not that hard anymore.

ByteBridge Labeling Instruction Template

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

ByteBridge 3D Point Cloud Annotation Tool

3D Point Cloud Annotation Types:

  • Sensor Fusion Cuboids: 49 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 Time-aligned 2D images could be provided, point clouds outputs only.

Advantages of Our 3D Point Cloud Annotation Service:

· Support 2D/3D sensor fusion, support multiple cameras

· Support scalable data annotation

· AI-powered sensor fusion tool: labeling at 2X-5X speed

· Ease of use QC tool: real-time revision and synchronous feedback

ByteBridge 3D Point Cloud QC Tool

Data Security

We comply with principles and rules in each region and we respect data the way your company does.

  • The CEO of the company supervises data management as a DPO (Data Protection Officer)
  • According to the guideline, if there is data leakage, we will inform the customer within 72 hours
  • GDPR personal privacy and data protection regulations compliance
  • Workers location, process, and authority restriction
  • No original data leak as the data is compressed and preprocessed
  • Support private cloud and privatization deployment
  • ISO27001 certification for information and facility security

Cost-effective

A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.

End

If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.

If you would like to have a look at the 3D point cloud live demo, please feel free to contact us: support@bytebridge.io

Source: https://k.sina.cn/article_5140861246_1326b513e001014foi.html?ab=qiche&_rewriteTime=1639319502386&http=fromhttp

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ByteBridge

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A data labeling platform with robust tools for real-time workflow management, providing high-quality training data with efficiency. — https://bytebridge.io/#/

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