Training Data for Self Driving Cars | RightClick.AI

Autonomous cars or self-driving cars have captured the interest of the people and is one of the major milestones in the automotive industry in recent times. All the big players in the automotive sector from the Volvo to Tesla, Mercedes, Benz and BMW have invested heavily in the development of self-driving cars. This has surely heated the autonomous vehicle race.

Machine Learning algorithms power these complex machines and help the cars to process visual data while driving in the same way as a human driver. The self-driving cars should be able to assign meaning to huge volumes of data for them to identify an obstacle such as other vehicles or pedestrians.

Labeled data is required for training ML algorithms for them to help the self-driving cars understand their surroundings. Machine Learning models are trained using labeled images of traffic, roads, signals, and others. Manual processing of images is a time-consuming process. Hence AI is used for processing and labeling of data in images. Compared to manual labeling, AI labeling is more accurate and quicker.

Below things are important when labeling data for autonomous vehicles;

Clarity

Clarity is required on the type of objects to be captured in images. For example, there will be various objects at a traffic intersection. In such a scenario, it is good to have guidelines on the type of objects that quality for labeling and capture the right criteria for labeling. This enables the annotating and labeling of the right objects efficiently and consistently.

Select the Right Toolsets

A different set of toolsets are required for each annotation task. For object localization and detection, bounding boxes work well whereas drawing cuboids and applying text labels work well for metadata attribution. Polylines work well for outlining roads and lane markings. Segmentation tasks do not work for these kinds of annotation tasks. Segmentation tasks work well for outlining overlapped objects and the ones with shared boundaries.

Economy

In the production environment, the scale of data labeling increases. This may increase the risk of bad data. The exponential increase in the requirement for training data at the production level is a challenge for companies. To tackle this challenge, companies have to invest in recruiting internal resources for data labeling tasks at scale. But, it may not be a feasible option for a single company.

In such scenarios, outsourcing your data labeling needs to third-parties like RightClick.AI is the best option. We have a team of data labeling experts who can manage your data labeling needs at scale.

About RightClick.AI

At RightClick.AI, we combine human care with technology to offer the best-quality image and video labeling services. We maintain the highest standards of quality while labeling images that will help to train and test self-driving models effectively.