Abstract

The road to reliable ground truth starts before any labeling is even made. In perception software development, when training a network, the specifications of the wanted function need to be translated to the labeling guideline. The function and its desired behavior need to be described in the labeling guideline to get the expected behavior from the model in the end.

During annotation, ambiguous situations frequently pop up; these must be dealt with, preferably before starting the labeling work. A guideline shall also be understood and interpreted in the same way by hundreds of persons, usually with different cultural backgrounds and education.

How can we achieve a solid guideline that captures all function needs and has no room for ambiguities, and how can we assure that the rules/requirements are fulfilled? This presentation will show a structured way to minimize the risk of ambiguities and inconsistency in the guidelines and the tools available at Annotell to help you.

Tommy Johansson

Perception Expert @ Annotell

Tommy has worked with AD/ADAS in a range of different OEMs and suppliers of AD/ADAS systems since 2006. In 2021, he joined Annotell because of the realization that the future of AD/ADAS will depend on the capabilities of deep learning and its safety argumentation.

At Annotell, he works as a Perception Expert, guiding their customers on achieving the ground truth they need with the quality they need. He is also managing a new team called Perception Research with the goal to identify and drive Annotell's unique position in making safe perception possible.