Generative AI to augment environmental conditions in training data for perception systems.

Purpose and goal:

The project aims to enhance the perception systems in autonomous systems by enriching image datasets using advanced generative AI techniques to create required variation in these training sets. It focuses on improving perception systems under varied environmental conditions with practical commercial verification.

Expected results and effects:

Our technology saves time for developers by automating data gathering and annotation from diverse conditions. Without our technology, autonomous developers require manual data procurement or recording and annotation which is hugely costly and time consuming. Generative AI automates this process without compromising on performance. The technology ultimately ensures autonomous systems can efficiently learn to adapt to dynamic environments, leading to safer and more effective operation.

Approach and implementation: The key components of our innovation are based on Machine Learning, and include:

Generative Models for Environmental Augmentation: Introducing varied weather and lighting conditions to simulate realistic environments. Temporal Consistency Techniques: Ensuring that augmented image sequences maintain coherent changes over time.

Enhanced Object Annotations: Automating the addition of new objects with semantic annotations to improve object detection and recognition capabilities.