In the rapidly advancing landscape of connected and automated vehicles (CAV), the integration of Vehicle-to-Everything (V2X) communication in traditional fusion systems presents a promising avenue for enhancing vehicle perception. Addressing current limitations with vehicle sensing, this paper proposes a novel Vehicle-to-Vehicle (V2V) enabled track management system that leverages the synergy between V2V signals and detections from radar and camera sensors. The core innovation lies in the creation of independent priority track lists, consisting of fused detections validated through V2V communication. This approach enables more flexible and resilient thresholds for track management, particularly in scenarios with numerous occlusions where the tracked objects move outside the field of view of the perception sensors. The proposed system considers the implications of falsification of V2X signals which is combated through an initial vehicle identification process using detection from perception sensors. Presented are the fusion algorithm, simulated environments, and validation mechanisms. Experimental results demonstrate the improved accuracy and robustness of the proposed system in common driving scenarios, highlighting its potential to advance the reliability and efficiency of autonomous vehicles.

翻译：在网联与自动驾驶车辆（CAV）快速发展的背景下，将车联网（V2X）通信集成到传统融合系统中，为增强车辆感知能力提供了一条有前景的途径。针对当前车辆感知的局限性，本文提出了一种新型基于车-车（V2V）通信的跟踪管理系统，该系统利用V2V信号与雷达及摄像头传感器检测数据之间的协同效应。核心创新在于构建独立的优先级跟踪列表，该列表由通过V2V通信验证的融合检测数据组成。这种方法能够为跟踪管理设置更灵活且更具鲁棒性的阈值，尤其适用于目标被遮挡而移出感知传感器视野的复杂场景。所提出的系统考虑了V2X信号伪造的影响，并通过利用感知传感器的初始车辆识别过程来应对这一问题。本文介绍了融合算法、仿真环境及验证机制。实验结果表明，所提系统在常见驾驶场景中具有更高的准确性和鲁棒性，凸显了其在提升自动驾驶车辆可靠性与效率方面的潜力。