How Data Labeling Service Empowers Intelligent Driving?

The rapid development of artificial intelligence cannot leave the support of high-quality datasets. Whether it is commercial applications or scientific research, datasets provide a continuous source of power for AI technology. Datasets aren’t only the input for algorithm training, but also the determining factor affecting the maturity of AI technology. By using real world data, researchers can train more robust AI model to handle various unpredictable scenario changes.

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Relevant agencies predict that by 2030, intelligent cars will account for more than 40% of the overall travel mileage, and the penetration rate of fully intelligent new cars can reach 10%. According to the data from the CPCA, the scale of China intelligent driving market will continue to grow in the next five years. By 2024, the scale of the intelligent driving market is expected to exceed 100 billion yuan. Driven by huge market dividends, numerous companies are competing on the track of intelligent driving.

Challenges of Intelligent Driving

Although the intelligent driving has been rapidly developed, it also faces many problems. At present, the main problem is that artificial intelligence is not so intelligent. Complex traffic scenes and uncontrollable random factors will challenge the perception of AI. Therefore, improving the perception of AI becomes the challenge of the future development of intelligent driving.

As is known, Data, Algorithms, and Computing are the three indispensable elements of AI. Among them, data plays a supporting role in enhancing the perception of AI. At present, the main way to solve the perception problem is to feed piles of training data to the algorithm through supervised learning, so that the algorithm models get universal perception capabilities. The balloons on the road, the dummy, the advertisement on the car body, the reflection of the buildings, etc., which may be difficult to imagine for us, also need to be fed to the algorithm one by one, to ensure that the algorithm can correctly perceive.

Intelligent Driving Data Solutions

As the first listed company in China’s AI data service industry, Nexdata’s core business is to provide integrated data solutions, including high-quality training datasets, data customization services, data labeling platform deployment, etc.

In the field of intelligent driving, Nexdata provides a variety of customized data labeling services:

2D Vehicle/Pedestrian/Traffic Sign Boxing

It’s applied to the basic recognition of vehicles and pedestrians.

Vehicle Polygon

Drivable area labeling and objec classification can train intelligent cars to follow other cars or change lanes on the road.

3D Cube

3D cube labeling can help to train intelligent cars to judge the volume of passing or overtaking vehicles.

Polyline

Polyline labeling can help to train intelligent cars to drive according to the lane rules

Semantic Segmentation

The segmentation of different areas can help intelligent cars to recognize the real-world drivable areas.

Point Cloud

Point cloud is mainly applied to the construction of VR training scenes for intelligent driving.

In addition, Nexdata supports customized data collection services, such as cockpit crew behavior collection, 2D street view data collection, multi-language and multi-crowd speech collection, etc.

With a variety of template tools, flexible coordination of manpower and algorithms, and professional data services, Nexdata will continue to spare no efforts to satisfy customers’ data processing needs.

If you need data collection and labeling services, please feel free to contact us: [email protected]

Facing with growing demand for data, companies and researchers need to constantly explore new data collection and annotation methods. AI technology can better cope with fast changing market demands only by continuously improving the quality of data. With the accelerated development of data-driven intelligent trends, we have reason to look forward to a more efficient, intelligent, and secure future.