The first category is Tesla, INO, XPeng, and LI AUTO, which represent self-development branch;
Second, traditional automobile enterprises, which are developed through cooperation with parts companies and technology companies;
The third is that technology companies provide full station solutions for autonomous driving, such as Huawei’s cooperation with a polar fox.
The first category is Tesla, Xpeng, INO, and other new vehicle forces. These companies have relatively complete autonomous development capacity and own the R&D teams and manufacturing capabilities. Among these enterprises, the most similar ability with Tesla is Xpeng, although the technical route is completely different.
It is based on laser radar and a high-precision map. The most similar capability is both companies adopt the self-developed automatic driving full-stack algorithm. Unlike many autopilot companies using Mobileye chips to realize the perception ability, both of them directly process data, directly identify lane lines, vehicles, people, and objects.
The perception, decision-making, and control are fully independently developed. Data iteration can not only improve decision-making and control ability but also improve environmental perception. The core of automatic driving is actually from perception to cognition, especially in complex traffic scenarios.
In the second category, many traditional car enterprises respond to the technological change of intelligent vehicles in a conservative way: small input and fast output. Through cooperation with parts suppliers or auto-driving technology companies, we can obtain high-level automatic driving ability and build sample cars.
This is mainly because many traditional car enterprises now mainly profit from traditional vehicles. If they give up the main business that makes the most money now and invests in future-oriented intelligent vehicles, especially high-level automatic driving, it is very risky. Therefore, in the choice of technical routes, they will choose the safest and safest route.
However, such a choice will also make enterprises face challenges in the competition of intelligent vehicle technology. Outsourcing technology makes it difficult for these car companies to have the iterative ability based on data. The large-scale dataset and the iterative ability based on data are the core competitiveness of Tesla and other companies.
The third is Huawei, Didi, Baidu, and other technology companies. These companies have the core technology and core algorithms of automatic driving. They can make the technology landing through cooperation with the vehicle enterprises, but the problem they face is also data collection.
After the mass production of intelligent vehicles, can these companies continuously obtain data to iterate the function and performance of automatic driving in daily operation?
The core competitiveness of autonomous driving is data-driven. If the data collection ability is limited by the vehicle enterprises, the technological progress of these companies will be limited. That’s why Xiaomi wants to build its own cars, Baidu, didi, and Huawei cooperate with the car companies in depth.
These companies with strong automatic driving technology will establish closer cooperation with the car enterprises in the future, even integrate the vehicle building capacity into their own system, so as to ensure that the data can be collected continuously and used to train their own algorithms, so as to make the algorithm more and more perfect.
In the field of artificial intelligence automatic driving, accurate environmental perception, and precise positioning are the key to reliable navigation, informed decision making, and safe driving of a self-driving vehicle in complex dynamic environments.
These two tasks need to acquire and process highly accurate and informative data in a real environment. To obtain this data, sensors, such as lidar or cameras, are usually fitted to unmanned vehicles or mobile measuring vehicles.
Traditionally, the image data captured by the camera can provide two-dimensional semantic and texture information, and it is low cost and high efficiency, which is one of the most common data in the perception task. However, the image data lacks 3D geographic information.
Therefore, the dense, accurate, 3D geographic information point cloud data collected by lidar is also applied to the perception task. In addition, lidar is not sensitive to changes in lighting conditions and can work during the day and night, even with strong light and shadow interference, which is the advantage of 3D point cloud data.
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