Bandwidth-Adaptive Feature Sharing for Cooperative LIDAR Object Detection
This work addresses situational awareness challenges for autonomous vehicles, but it is incremental as it builds on prior feature sharing approaches.
The paper tackles the problem of limited network capacity in cooperative LIDAR object detection for connected autonomous vehicles by proposing a bandwidth-adaptive feature sharing mechanism and a decentralized data alignment method, resulting in improved average precision over a previous method on the Volony dataset.
Situational awareness as a necessity in the connected and autonomous vehicles (CAV) domain is the subject of a significant number of researches in recent years. The driver's safety is directly dependent on the robustness, reliability, and scalability of such systems. Cooperative mechanisms have provided a solution to improve situational awareness by utilizing high speed wireless vehicular networks. These mechanisms mitigate problems such as occlusion and sensor range limitation. However, the network capacity is a factor determining the maximum amount of information being shared among cooperative entities. The notion of feature sharing, proposed in our previous work, aims to address these challenges by maintaining a balance between computation and communication load. In this work, we propose a mechanism to add flexibility in adapting to communication channel capacity and a novel decentralized shared data alignment method to further improve cooperative object detection performance. The performance of the proposed framework is verified through experiments on Volony dataset. The results confirm that our proposed framework outperforms our previous cooperative object detection method (FS-COD) in terms of average precision.