EMCoP: Efficient Multi-Task Cooperative Perception with Reinforcement Learning

Cooperative perception helps connected agents understand their surroundings by exchanging information from different viewpoints. Existing cooperative perception methods often optimize sharing for individual perception tasks. However, agents need to run multiple tasks simultaneously to support safety-critical decisions such as path planning and obstacle avoidance. For instance, combining a surrounding object’s position, its expected movement and road layout helps an agent plan a feasible path. A naive extension of state-of-the-art frameworks would be to exchange information for all tasks independently, imposing heavy strain on shared bandwidth. However, tasks can provide overlapping information: e.g., detection and segmentation can both indicate that a region is occupied. If either provides enough information to plan around it, sharing additional features from both may improve their separate predictions without improving the path. Motivated by this insight, we introduce EMCoP, which trains a single reinforcement-learning policy to consider all tasks together when deciding how much information each task should share in the current scene. By evaluating the decision space through its effect on the agent's planned path, the policy learns which combinations keep path errors and predicted collisions low while minimizing communication. EMCoP further reduces communication by identifying recurring situations where recently received information remains sufficient. We evaluate EMCoP in cooperative driving on V2X-Sim. Extensive experiments demonstrate that EMCoP is able to reduce communication volume by up to 85% relative to full sharing, where every agent broadcasts all task information without selection.