Self-driving cars also known as autonomous cars have captured the imagination of the people and mark a major change in the automotive industry. With all the major players in the automotive industry from the Tesla to Volvo, BMW, Mercedes Benz, and others investing heavily in developing self-driving cars have heated the autonomous vehicle race.

The self-driving cars which are complex machines are powered by machine learning algorithms. These algorithms help the car to process a lot of visual data similar to how a human driver does while driving. For the vehicles to identify an obstacle such as a tree or a pedestrian, it should be capable of assigning meaning to large volumes of data.

Machine Learning algorithms have to be trained with labeled data to help the self-driving cars to understand its environment. Manual labeling of objects in images is a time-consuming process. Hence Artificial Intelligence is used for image processing and labeling of data. Labeling with AI is quicker and more accurate than manual labeling.

Few things have to be considered when annotating and labeling autonomous vehicular data;

Clarity

There should be proper clarity on what objects to capture. For instance, there may be many objects at a traffic intersection. In this kind of scenario, it is best to have guidelines on what objects can qualify for labeling and capturing the right criteria for them. This helps to annotate and label the right objects and leads to efficiency and consistency.

Select the Right Toolsets

Each annotation task requires a different set of toolsets. Bounding Boxes work well for object localization and detection whereas applying text labels & drawing cuboids are required for metadata attribution. Polylines are used to outline roads and lane markings. The tools that you may use for these annotation tasks will not work for segmentation tasks. The segmentation tasks require outlining of overlapped objects and the ones that share boundaries.

Economy

The scale of data annotation need increases in the production environment. This, in turn, increases the risk of bad data. The exponential increase in training data needs at the production level has turned out to be a challenge for companies.  They have to hire enough internal resources to handle the data labeling tasks at a scale which is not feasible for a single company.

The best option is to outsource your data annotation and labeling needs to third-parties like Data Labeler who with a team of more than 1000 full-time data labelers can manage your annotation needs at scale.

At Data Labeler, we combine technology with human care to provide annotations and labeling of images and videos with pixel accuracy. Our data labelers maintain quality while processing & labeling the image data required to train and test your self-driving cars effectively.