Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication

Deep learning has been widely used in intelligent vehicle driving perception systems, such as 3D object detection. One promising technique is Cooperative Perception, which leverages Vehicle-to-Vehicle (V2V) communication to share deep learning-based features among vehicles. However, most cooperative perception algorithms assume ideal communication and do not consider the impact of Lossy Communication (LC), which is very common in the real world, on feature sharing. In this paper, we explore the effects of LC on Cooperative Perception and propose a novel approach to mitigate these effects. Our approach includes an LC-aware Repair Network (LCRN) and a V2V Attention Module (V2VAM) with intra-vehicle attention and uncertainty-aware inter-vehicle attention. We demonstrate the effectiveness of our approach on the public OPV2V dataset (a digital-twin simulated dataset) using point cloud-based 3D object detection. Our results show that our approach improves detection performance under lossy V2V communication. Specifically, our proposed method achieves a significant improvement in Average Precision compared to the state-of-the-art cooperative perception algorithms, which proves the capability of our approach to effectively mitigate the negative impact of LC and enhance the interaction between the ego vehicle and other vehicles.

Automated summary

This paper studies the impact of Lossy Communication on Cooperative Perception and proposes a new approach to mitigate these effects. The approach includes an LC-aware Repair Network and a V2V Attention Module with intra-vehicle attention and uncertainty-aware inter-vehicle attention. The results demonstrate that the proposed method improves detection performance under lossy V2V communication, proving its capability to effectively mitigate the negative impact of LC and enhance the interaction between the ego vehicle and other vehicles.