Perception Module

Perception modules are crucial components of autonomous systems, aiming to accurately interpret sensor data (visual, audio, depth, etc.) and provide reliable information for decision-making. Current research emphasizes improving the robustness and generalization of these modules, often employing deep learning architectures like transformers and convolutional neural networks, and exploring techniques like self-supervised learning and multi-modal fusion to enhance performance. This work is vital for advancing autonomous driving, robotics, and other applications requiring reliable real-time environmental understanding, particularly in addressing challenges like perception errors and sim-to-real transfer.