Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks
This addresses scalability and reliability problems in cooperative object detection for autonomous vehicles, though it appears incremental as it builds on existing neural network and communication methods.
The paper tackles the scalability and reliability issues in cooperative object detection for autonomous vehicles by introducing feature sharing, which improves environmental understanding while balancing computation and communication loads. Experiments on the Volony dataset show significant performance superiority over conventional single-vehicle approaches.
The recent advancements in communication and computational systems has led to significant improvement of situational awareness in connected and autonomous vehicles. Computationally efficient neural networks and high speed wireless vehicular networks have been some of the main contributors to this improvement. However, scalability and reliability issues caused by inherent limitations of sensory and communication systems are still challenging problems. In this paper, we aim to mitigate the effects of these limitations by introducing the concept of feature sharing for cooperative object detection (FS-COD). In our proposed approach, a better understanding of the environment is achieved by sharing partially processed data between cooperative vehicles while maintaining a balance between computation and communication load. This approach is different from current methods of map sharing, or sharing of raw data which are not scalable. The performance of the proposed approach is verified through experiments on Volony dataset. It is shown that the proposed approach has significant performance superiority over the conventional single-vehicle object detection approaches.