Nerd For Tech
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Nerd For Tech

How 3D Point Cloud Annotation Service Fuels the Field of Automatic Driving?

With the rapid development of driverless technology, all kinds of autonomous robots carrying the technology, such as unmanned distribution vehicles and unmanned disinfection vehicles, rush to the front line of anti-epidemic and build a safe and contactless protective barrier.

More info: Baidu Apollo’s Driverless Car Joined in the Anti-epidemic Fight to Help Guangzhou Citizens’ Material Distribution and Travel

In driverless technology, the environment perception system acts as the “eyes” of the driverless vehicle. It mainly obtains the external environment information through the external sensors loaded on the vehicle, and accurately and quickly transmits the geographic information and obstacle information to the computer control system. The system can be operated safely without any human initiative.

An unmanned dining car should at least include sensors, a distribution cabinet, and a driving system equipped with AI technology. In order to make the unmanned vehicle “see”, the crucial sensor system needs to be equipped with LiDAR, camera, fisheye lens, radar, ultrasonic system, etc.

At present, the 3D modeling of the environment around the vehicle is mainly carried out by LiDAR so as to provide the basic information for the driving decision of the unmanned vehicle.

The camera, which can record 2D and 3D images and video, is a pure vision technology and can be used to convey the color and shape of obstacles, and the cost is low.

The disadvantage is the high requirements for natural environmental conditions. For example, the detection results at night will be worse than those in the daytime.

LiDAR generates point clouds, which can be used to detect almost all suspicious obstacles, even at night. The disadvantage is the high cost, and it is not reliable in bad weather like a foggy day.

More info: What’s LiDAR and What’s 3D Point Cloud?

Radar uses long-wavelength radio waves to detect long-distance obstacles and can be used in almost any natural environment. The disadvantage is the low precision, and it is not good at classifying obstacles.

Data Annotation Service Fuels the Field of Automatic 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.

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.

3D point cloud image labeled data is the basic training data of driverless technology. 3D point cloud annotation is considered the most appropriate for precise detection through LiDAR sensors.

3D point cloud image annotation is to label the target object in a 3D image collected by LiDAR sensors, using 3D boxing. The target includes vehicles, pedestrians, traffic signs and trees, etc.

End

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1 4 Insights in Autonomous Vehicles Car Industry

2 Can We Buy Self-driving Cars Right Now?

3 Three Categories of Self-driving Car Companies

4 Three Reasons for the Growing Demand of 3D Point Cloud Data in Automatic Driving in 2021

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