With the development of automatic driving technology, on L3 and L4 levels, it is necessary for vehicles to quickly and accurately identify the driving environments, including maps, moving and fixed objects, such as other vehicles, cyclists, pedestrians, traffic lights, and various roads. Of course, it also includes weather environment and in-car environment, such as passenger behavior, gesture or voice command, etc.
The realization of automatic driving technology is to teach cars to recognize this information and make correct judgments.
The difficulty of making decisions by autopilot cars is increasing. Data is not only the basis of vehicle recognition but also the key to the development of automatic driving technology.
The Data Collection Performance of Hardware Devices is Better
With the rapid development of 3D acquisition technology, more and more 3D sensors are available and affordable. The sensing system of self-driving vehicles has included various types of 3D scanners, lidar, and RGB-D cameras to carry out the perception of the surrounding environment.
Is the Lidar the Future of Automatic Driving?
In the field of automatic driving, accurate environmental perception and precise positioning are the keys to reliable navigation, informed decision-making, and safe driving in complex dynamic environments.
These two tasks need to acquire and process highly accurate and informative data in a real environment. In order to obtain such data, unmanned vehicles or mobile measurement vehicles are usually equipped with a variety of sensors, such as lidar or camera.
Traditionally, camera-captured image data can provide two-dimensional semantic and texture information with low cost and high efficiency, which is one of the most commonly used data in perception tasks.
However, the image data lacks 3D geographic information. Therefore, the dense, accurate point cloud data with 3D geographic information collected by lidar is also used in perception tasks. In addition, lidar is insensitive to changes in lighting conditions and can work in the daytime and at night, even with strong light and shadow disturbance, which is also the advantage of 3D point cloud data.
3D Data Can Provide More Dimensional Information
3D data obtained by these sensors can provide abundant geometry, shape, and scale information; Side by 2D images, 3D data provides an opportunity to better understand the environment.
Three-dimensional data can usually be expressed in different formats, including depth image, point cloud, and volume grid. As a common format, the point cloud retains the original geometric information in three-dimensional space without any discretization. Therefore, it is the preferred representation for many scenarios.
What is 3D Point Cloud?
3D Point Cloud is widely used for product development and analysis in fields related to architecture, aerospace, driving, traffic, medical equipment, regular consumer items, and more. The potential use cases and applications are only expected to increase in the future.
For all the moving objects in the radar map, each target object in the map is selected in the form of a 3D frame, which is subdivided into 11 categories: car, truck, heavy vehicle, two-wheeled vehicle, pedestrian, etc.
Many Internet Companies Enter the Automobile Industry in China
What Changes are They Bringing to the Automatic Driving Industry?
The development of smart cars is inseparable from big data, the Internet, and cloud computing, which are the strengths of ICT enterprises in China. More importantly, the entry of ICT enterprises into the automotive industry has brought a lot of capital, a lot of talents, and some advanced concepts, such as software-defined automobiles, rapid iteration, and so on.
This new concept will have a great impact on the traditional automobile industry, and will also bring great help.
It is necessary to remind ICT enterprises that when they enter the automobile industry, they should pay attention to the close integration with traditional automobile manufacturers. Automobiles are complex industrial products, involving production process problems, such as product quality and reliability. In terms of manufacturing, process, and quality inspection, it takes a long time and a lot of accumulation, these are the advantages of traditional automobile manufacturing enterprises.
The combination of ICT’s capital, talent, and concept advantages with the manufacturing capacity and channel will bring greater competitive advantages to the development of self-driving vehicles.
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: email@example.com
Resource：21st century economic report