Multi Agent Perception

Multi-agent perception (MAP) aims to improve environmental understanding by fusing data from multiple autonomous agents, overcoming limitations of single-agent sensing like occlusions and limited range. Current research focuses on robust methods for data fusion, addressing challenges such as inconsistent localization, sensor failures, and differing model architectures across agents, often employing transformer-based architectures and contrastive learning techniques. This field is crucial for advancing autonomous systems, particularly in applications like self-driving cars and robotics, by enabling more reliable and comprehensive perception in complex and dynamic environments.