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Training Data for Autonomous Driving


Using processed images, algorithms learn to recognize the real environment for autonomous driving.

Philip Kessler at the Karlsruhe Institute of Technology in Germany has launched a startup that improves and accelerates the labeling of image elements for autonomous driving algorithms.

Credit: understand.ai

Philip Kessler at the Karlsruhe Institute of Technology (KIT) in Germany has launched understand.ai, a startup that improves and accelerates the labeling of image elements for autonomous driving algorithms.

These labels, also called annotations, must agree with the real environment with pixel accuracy. The better the quality of the processed image data, the better the algorithm will be at using the data for training.

Traditionally, objects in images are labeled manually by human staff, but the process is troublesome and time-consuming; artificial intelligence makes the labeling process up to 10 times quicker and more precise.

Said Kessler, "As training images cannot be supplied for all situations, such as accidents, we now also offer simulations based on real data."

From Karlsruhe Institute of Technology
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Abstracts Copyright © 2019 SmithBucklin, Washington, DC, USA


 

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