Robust Perception

Robust perception aims to develop artificial systems capable of reliably interpreting sensory information, even under challenging conditions like adverse weather, sensor malfunctions, or unexpected events. Current research focuses on improving the robustness of perception models through techniques such as multi-sensor fusion (combining data from cameras, lidar, radar), data augmentation (synthetically generating diverse training data), and the development of novel architectures like diffusion models and vision transformers. These advancements are crucial for enabling safe and reliable operation of autonomous vehicles, assistive robotics, and other applications requiring dependable environmental understanding.