Sensor Selection for Multi-Level Collaborative Perception with Covariance Intersection

Collaborative perception technology enhances the perception range and accuracy in complex scenarios by enabling vehicles to share their sensor data. However, due to communication bandwidth limitations, it is important to prioritize the transmission of the most critical sensor data. In the context of multi-target tracking, this paper proposes a multi-level sensor scheduling framework for collaborative perception. The framework quantifies the collaborative gain from object-level, featurelevel, and track-level data across different collaborative vehicles, and employs covariance intersection to address the issue of unknown correlations in multi-vehicle collaborative perception and tracking. By introducing auxiliary variables and employing relaxation techniques, the original scheduling problem is transformed into a convex optimization problem, resulting in a low-complexity sensor selection algorithm. Simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithms. Ablation experiments further reveal that adding feature-level and track-level fusion on top of object-level fusion yields significant collaborative gains.