As autonomous systems continue to evolve, the diversity and complexity of required annotations have grown exponentially. Challenges are ranging from multi-sensor fusion data and complex scene understanding to early fusion approaches and voxel representation support. Modern annotation processes demand the right expertise, tooling, workflows and workforce to deliver high-quality datasets at scale. This session delves into the key types of annotations currently seen in large-scale automotive and robotics projects and how an integrated approach can optimize accuracy, speed, and scalability in data labeling.
In this session, we will:
- Explore the range of annotation types, including LiDAR, radar, and multi-sensor data labeling
- Understand how to implement hybrid workflows combining manual, ML-assisted, and automated tooling for greater efficiency
- Gain insights into how data platforms allow for the next generation of data labeling in autonomous systems
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