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You can Doubt the Safety of Autonomous Driving, but Big Data Tells You: It Does Drive Better than You — Part2

In the previous article, we learn that even if a traffic accident has occurred, the risk of injury and death will be greatly reduced with the intervention of autonomous driving.

At the current stage, it is a fact that autonomous driving performs better than humans.

Autonomous driving is entering the tipping point of maturity

The facts verified show that autonomous driving is gaining wider applications, which has been approved by a sufficiently large number of cases.

The commercialization of autonomous driving should be divided into the passenger car field, the commercial vehicle and operating vehicle field.

OEMs have begun to make process on L3-level autonomous driving in the passenger car market. And L2 level intelligent driving assistance functions are close to popularization. Xin Guobin, Vice Minister of the Ministry of Industry and Information Technology of China, said in the first half of this year, “The market share of cars with L2 level intelligent driving assistance functions has exceeded 20%.”

December 10th 2021 was a day worth remembering in the history of autonomous driving. The German regulatory agency officially released L3 conditional autonomous driving. In commercial vehicles and operating vehicles field, expecially in specific scenarios such as retail, Robotaxi, mining trucks, airports, and logistics, L4 autonomous vehicles or unmanned vehicles have been deployed in specific places such as terminals, airports, and hierarchical open roads.

2021 had entered the tipping point for the official legalization of autonomous driving

On March 24, 2021, the Ministry of Public Security of China(MPS)issued a public comment on the “Road Traffic Safety Law,” which clarified the relevant requirements for road testing, road passage of vehicles with autonomous driving functions and the sharing of liabilities for violations and accidents. The regulations give autonomous driving systems and road testing legal status and establish a legal environment for the large-scale commercial use of autonomous driving.

Liability for violations and accidents of autonomous driving will soon be incorporated into the Road Traffic Safety Law, further improving the governance model of autonomous driving.

Among them, Article 155 stipulates: “Automatically driving vehicles shall conduct road tests on closed roads and venues, obtain temporary driving license plates, and conduct road tests at designated times, areas, and routes in accordance with regulations. Those who have passed the test are permitted to produce, import, and sell in accordance with relevant laws and regulations. It has cleared away obstacles in the production and sales of autonomous vehicles.

Of course, at the level of laws and regulations, there are currently no comprehensive rules for the division of liability for self-driving car accidents in all countries worldwide. The “Road Traffic Safety Law of the People’s Republic of China” and the “Implementation Regulations of the Road Traffic Safety Law of the People’s Republic of China” do not involve any autonomous driving safety aspects, either. At this stage, traffic violations and accidents during road testing and demonstration shall be dealt with under current laws.

However, in view of the conditional automatic driving release of L3 level in Germany, the OEMS would take the legal responsibility under the automatic driving state.

Data is meaningful only if it is well labeled

The mainstream algorithm model of autonomous driving is mainly based on supervised deep learning. It is an algorithm model that derives the functional relationship between known variables and dependent variables. A large amount of structured labeled data is required to train and tune the model.

On this basis, if you want to make self-driving cars more “intelligent”, and form a closed loop of the business model for self-driving applications that can be replicated in different vertical landing scenarios, the model needs to be supported by massive and high-quality real road data.

In the field of autonomous driving, data annotation scenes usually include changing lanes and overtaking, passing intersections, unprotected left and right turn without traffic light control, and some complex long-tail scenes such as vehicles running red lights, pedestrians crossing the road, and roadsides as well as illegally parked vehicles, etc.

The current artificial intelligence is also called data intelligence. At this stage of development, the more layers of the neural network, the larger amount of labeled data is needed.

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

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://aikahao.xcar.com.cn/item/1069969.html

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NFT is an Educational Media House. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. To know more about us, visit https://www.nerdfortech.org/.

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ByteBridge

ByteBridge

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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