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Why did Germany and Mercedes-Benz Give the Green Light for L3 Autonomous Driving Together? -Part4

In terms of liability determination, although some car companies have claimed that auto companies should hold responsibility for accidents in the process of autonomous driving, such claims don’t have authority due to the lack of approval from local market laws.

As Germany passed the law to “loosen” the L3 level, the balanced risk distribution among drivers, car owners, and manufacturers formed a three-pillar liability system. The automaker was officially included in the scope of responsibility.

It can be said that in the groping stage of the autonomous driving governance model, not only the German government has actively promoted it, but behind it is Daimler’s active cooperation and active social dialogue.

“Car manufacturers cannot answer all the questions surrounding autonomous driving themselves, including those involving ethics. These must be discussed as part of a wider debate.” Daimler Renata Jungo Brüngger said. In addition to technology, Daimler’s multidisciplinary steering committee has recruited developers and engineers and a team of legal experts specializing in autonomous driving to deal with issues related to law, ethics, and data protection. Indeed, the first mass production and commercialization of L3 in Germany result from the joint promotion of the government and car companies.

Today Daimler and Mercedes-Benz are delighted to become “the world’s first manufacturer in Germany to put conditional autonomous driving into mass production.” which is very important for boosting the confidence of the German automobile industry in the era of intelligent driving. It is not difficult to achieve the L3 level technically, but it is challenging to establish laws and regulations.

Tesla has the technology, but it has reserved a route of retreat itself in the level classification. The A8 launched by Volkswagen Audi that year was initially the first mass-produced model to claim L3 autonomous driving. Still, it finally compromised on the delivery mode and abandoned the attempt of L3’s first production car. Japan’s Honda has also provided a similar solution in a premium model named Legend. We believe that in 2022, there will be more announcements of L3 vehicles, just like China’s GAC Trumpchi announced last year. But mass production is still yet to come.

The German regulatory authorities have taken the lead in the world and have provided references for countries worldwide. It is believed to encourage more countries to promote the era of future transportation. Mercedes-Benz won the first company’s name, and it will also face the world’s onlookers, scrutiny, and strict tests in 2022.

The Industrialization Focus of Autonomous Driving Scenarios: AI Data

2D-3D fusion data:

For example, in order to develope multi-model machine learning algorithms for self-driving cars, some manufacturers need to fuse two distinct data sets with different dimensions. This operation is essential, but it is challenging to perform manually.

AI companies even hope that data companies can better understand algorithm technology and demand scenarios, participate in the research and development of algorithms, and give optimization suggestions on data collection. It has become the focus of data service providers to create competitive advatange as well.

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

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Communication Cost Saving

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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

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· 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


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