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Types of best Data Labeling for Automated Cars

The effectiveness of machine learning Automated depends on various factors, one of which is data labeling. In automotive self-driving applications, precise vision training is required to detect or recognize objects on the road and keep the car in the correct lane. So, what are the types of data labeling for autonomous driving? Let’s introduce it below.

Data annotation is used for the purpose of autopilot:

self driving cars scaled

1. Monitor the behavior in the car

Safety and user experience can be greatly improved Automated by implementing AI in-vehicle monitoring. For passengers, it is possible to detect human facial expressions and track passenger movements. For drivers, it can detect the driver’s situation, such as reminding them when they are asleep or distracted, and prevent accidents.

2. Vehicle identification

One of the main goals of self-driving cars is to recognize other vehicles and navigate around them. This identification process must be 100% identifiable to ensure absolute security. And low-light conditions, especially night driving, can severely impair computer vision models that have not been exposed to the realities of night driving.

3. Computer vision for self-driving cars

Self-driving cars also need a comprehensive understanding of the environment in which they drive. This means being able to distinguish between the road, the sidewalk and the sky. In Automated complex and busy urban environments, AI systems are influenced by vast amounts of sensory data.

Types of data labeling for autopilot:

mini automatic hatchback

1. 3D cube labeling

3D cube labeling is to further 3D label the Automated vehicles in the 2D pictures, and is mainly used to judge the volume of vehicles passing by during driving.

2. Polygon labeling

Vehicle polygon annotation can accurately mark the shape information of the vehicle, which can be applied to the identification of vehicle types, such as vans, trucks, buses, cars, etc., to train automatic driving, and to selectively follow cars or change lanes on the road.

3. Semantic Segmentation

Semantic segmentation is a relatively broad type of labeling, which is to segment and label different areas in the picture, which can help intelligent driving vehicles identify drivable areas on the road.

4. Marking of lane lines

Lane line labeling is a comprehensive labeling of road ground markings, including classification labeling, area labeling and semantic labeling. It is used to label lane lines in intelligent Automated driving scenarios, so that intelligent driving vehicles can follow the lane lines. driving according to the rules.

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