You Can Doubt the Safety of Autonomous Driving, but Big Data Tells You: It Does Drive Better than You— Part3
Consumers Need an Open Understanding of the Vision “Zero Casualties” Portrayed by Autonomous Driving
Autonomous driving described a vision of “zero casualties” for humans in the early propaganda, which caused a misunderstanding to the public to a certain extent.
In recent years, accidents related to autonomous driving, on the one hand, differed from the public’s awareness and understanding of ADAS functions and fully autonomous driving. On the other hand, the expectation for autonomous driving is too high.
People accept autonomous driving, which means we must accept a machine that makes mistakes. Under this conception, human beings have set the responsibility of the main engine factory or the intelligent driving system and have corresponding compensation standards.
A five million RMB（746,000 USD)compulsory traffic accident liability insurance has been set for autonomous driving road tests in China’s corresponding areas. For tests that require manned demonstration applications, it is mandatory to purchase seat insurance and personal accident insurance for the passengers, and other necessary commercial insurance. Commercial insurance sets safety standards for autonomous vehicles to enter ordinary public roads, which means that the responsibility for accidents between humans and machines is defined economically.
Recognizing that machines will make mistakes and acknowledging that even future L5 autonomous driving may not guarantee 100% zero casualties are the prerequisites for humans to get along with autonomous vehicles. With this premise, humans will eventually find that traveling by machines greatly enhances safety.
Therefore, “zero casualties” will always be the highest goal pursued by autonomous driving, but it will not be achieved 100%.
At the end of last year, Germany started the competition to open up the L3 autonomous driving governance model, and Mercedes-Benz became the world’s first OEM of mass-produced autonomous vehicles protected by law. Now that this step has been taken, transportation has entered a new era.
At present, autonomous driving is entering a critical point of maturity in terms of technology, testing, manufacturing, laws, and regulations. The auto powers represented by China, the United States, Europe, Japan, and South Korea are competing to seize the commanding heights of the industry. They are also promoting each other to form a self-driving social governance model that has special and mutually inclusive characteristics.
Autonomous driving technology is now the main track of technology competition in different countries. An international governance model around smart cars is also being formed in the competition. For clients, autonomous vehicles in various countries can follow the same standards is the best result.
The Industrialization Focus of Autonomous Driving
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.
2D-3D Fusion Data:
For example, in order to develop 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 a competitive advantage as well.
Common Data Labeling Types Include:
- 2D Bounding Boxes
- Lane Marking
- Video tracking annotation
- Point Annotation
- Semantic Segmentation
- 3D Object Recognition
- 3D Segmentation
- Sensor Fusion: Sensor Fusion Cuboids/Sensor Fusion Segmentation/Sensor Fusion Cuboids Tracking
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.
- 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.
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.
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
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
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: firstname.lastname@example.org