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

Abstract: Deep learning has been widely used in the perception (e.g., 3D object detection) of intelligent vehicle driving. Due to the beneficial Vehicle-to-Vehicle (V2V) communication, the deep learning based features from other agents can be shared to the ego vehicle so as to improve the perception of the ego vehicle. It is named as Cooperative Perception in the V2V research, whose algorithms have been dramatically advanced recently. However, all the existing cooperative perception algorithms assume the ideal V2V communication without considering the possible lossy shared features because of the Lossy Communication (LC) which is common in the complex real-world driving scenarios. In this paper, we first study the side effect (e.g., detection performance drop) by the lossy communication in the V2V Cooperative Perception, and then we propose a novel intermediate LC-aware feature fusion method to relieve the side effect of lossy communication by a LC-aware Repair Network (LCRN) and enhance the interaction between the ego vehicle and other vehicles by a specially designed V2V Attention Module (V2VAM) including intra-vehicle attention of ego vehicle and uncertainty-aware inter-vehicle attention. The extensive experiment on the public cooperative perception dataset OPV2V (based on digital-twin CARLA simulator) demonstrates that the proposed method is quite effective for the cooperative point cloud based 3D object detection under lossy V2V communication.

• The paper introduces a LC-aware Repair Network and V2V Attention Module to recover and enhance features lost due to lossy V2V communication.
• The methodology utilizes an encoder-decoder framework with attention mechanisms to effectively mitigate communication-induced feature degradation.
• Experimental results on the OPV2V dataset with the CARLA simulator demonstrate significant improvements in 3D object detection performance over state-of-the-art methods.

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

Introduction

The paper "Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication" (2212.08273) addresses the impact of lossy communication on cooperative perception systems in the field of autonomous driving. These systems leverage Vehicle-to-Vehicle (V2V) communication to enhance perception capabilities by sharing deep learning-based features across vehicles. The authors contend that existing cooperative perception algorithms assume ideal communication scenarios, disregarding the prevalent issue of lossy communication that can significantly degrade perception performance.

Figure 1: Illustration of the V2V cooperative perception pipeline and its detection performance drop suffering from lossy communication on the public digital-twin CARLA simulator.

Methodology

The proposed methodology introduces a novel intermediate feature fusion framework designed to counteract the detrimental effects of lossy communication. This is accomplished through the development of two key components: the LC-aware Repair Network (LCRN) and the V2V Attention Module (V2VAM). The LCRN operates on an encoder-decoder architecture and aims to recover incomplete features caused by lossy communication by utilizing tensor-wise filtering. Meanwhile, the V2VAM enhances interactions between the ego vehicle's features and those from its surrounding vehicles, employing attention mechanisms to accommodate the uncertainties introduced by lossy transmission.

Figure 2: The architecture of LC-aware feature fusion framework. The proposed model includes five components: Metadata Sharing, Feature Extraction, Feature Sharing, LC-aware Repair Network, and V2V Attention Module.

Experimental Results

Experiments were conducted using the OPV2V dataset, which is based on the CARLA simulator. The proposed architecture demonstrated substantial improvements in 3D object detection tasks under lossy communication scenarios, outperforming prominent intermediate fusion methods. In particular, the approach achieved significant enhancements in Average Precision (AP) over state-of-the-art cooperative perception algorithms, showcasing its capability to effectively alleviate communication-induced feature degradation.

Figure 3: The LC-aware Repair architecture for feature recovery is based on the encoder-decoder structure, which outputs per-tensor feature kernels.

Implications and Future Directions

This research provides both theoretical and practical advancements in the domain of intelligent vehicle perception systems. The methodologies proposed not only improve robustness in communication-challenged environments but also lay the groundwork for further exploration into adaptive perception systems that can dynamically respond to fluctuating communication conditions. Future developments could focus on integrating these features into real-world CAV systems, refining the recovery mechanisms, and exploring additional attention-based fusion strategies to optimize communication resilience further.

Conclusion

The paper presents essential findings that advance the understanding and capabilities of V2V cooperative perception systems under non-ideal communication settings. By incorporating mechanisms such as the LC-aware Repair Network and V2V Attention Module, this research significantly enhances the reliability and effectiveness of autonomous driving systems operating in complex real-world scenarios.