Published on: 2022-10-27
Written by the Enlabeler team
Over recent years, the pursuit of the best autonomous vehicles has invigorated public imagination and aroused unprecedented collaboration between vehicle manufacturers and tech innovators. Forecasts predict that the autonomous vehicle market will grow from USD 1.64 Billion in 2021 to USD 11.03 Billion by 2028, and a CAGR growth of 31.1% is expected from 2021 to 2028.
In reality, a complex puzzle of machine learning algorithms, sensors, automatic parts, and various other pieces need to be correctly assembled for autonomous vehicles to become an integrated part of our roads. The machine learning algorithms that drive automated vehicles are able to analyse and navigate their surroundings because they are trained with quality annotated video and image data. Annotated data is derived from human labelers or machine learning models adding information to raw images, these labels enable computer vision AI (artificial intelligence) to contextualise their surroundings.
The types of data annotations used in annotating for Autonomous Vehicles are:
- 2D Bounding box
Bounding boxes are used in object detection, where the goal is to identify the position and type of object in an image without being too complex and annotating the edges of the subject.
- Polygons
The polygon annotations can be used to identify the location and class of objects with complex shapes. This is one of the more commonly used types of annotation as it allows the annotators to capture irregular shapes and objects that are partially concealed with pixel-perfect precision.
- Semantic segmentation
Semantic image segmentation identifies each pixel of an image with a corresponding distinct class. For example, a self-driving car is required to understand its surrounding environment in order to navigate any real-life scenarios; the model might require to identify different segments of the road.
- Key point
Key point annotation involves placing numbered or named dots on specific locations on an image. For example, in the case of a human, this can be the head, hands feet, and other key connection points. Keypoint annotations are very well suited for movement tracking and inflection points. For example of self-driving car models, key points annotations can help detect human patterns for pedestrians to understand behavior and movements.
- Polyline
The polyline tool is used to identify leading lines on images. These lines allow for the accurate recognition of the path ahead of self-driving cars. Polylines are an essential part of training data sets for reliable and safe autonomous vehicle AI models as this ensures the cars are able to navigate through traffic while still following lane discipline.
- Cuboids
3D cuboids are similar to bounding boxes, however, this tool allows you to get a 3D representation of the object and offer additional information on the depth, distance, and volume of the object. In the case of autonomous vehicles, this information can be used to measure the distance of objects from the car.
- 3D Point Cloud
3D LiDAR (Light Detection and Ranging) annotation is also known as point cloud labeling and uses laser type sensors to “see” the world in 3D. A standard LiDAR sensor emits a pulse of light into the surroundings of the vehicle, these pulses reflect off the objects and back to the sensor. The sensor uses the time taken for each pulse to return to determine how far the object is. By emitting the pulses millions of times per second an accurate 3D representation of the surveyed environment is created. How 3D point cloud annotation is applied in driverless technology is by using image annotation to label the target object using a 3D image that is captured by LiDAR sensors. This detects objects such as vehicles, pedestrians, traffic signs, and trees.
Conclusion
The rapid rise and development of self-driving cars have created a demand for driverless technology. Data labeling is thus crucial for creating a safe environment for these unmanned vehicles especially when it comes to predicting and managing the environment perception using the different annotation tools that make for a successful self-driving vehicle market.
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