Cooperative perception can significantly improve the perception performance of autonomous vehicles beyond the limited perception ability of individual vehicles by exchanging information with neighbor agents through V2X communication. However, most existing work assume ideal communication among agents, ignoring the significant and common \textit{interruption issues} caused by imperfect V2X communication, where cooperation agents can not receive cooperative messages successfully and thus fail to achieve cooperative perception, leading to safety risks. To fully reap the benefits of cooperative perception in practice, we propose V2X communication INterruption-aware COoperative Perception (V2X-INCOP), a cooperative perception system robust to communication interruption for V2X communication-aided autonomous driving, which leverages historical cooperation information to recover missing information due to the interruptions and alleviate the impact of the interruption issue. To achieve comprehensive recovery, we design a communication-adaptive multi-scale spatial-temporal prediction model to extract multi-scale spatial-temporal features based on V2X communication conditions and capture the most significant information for the prediction of the missing information. To further improve recovery performance, we adopt a knowledge distillation framework to give explicit and direct supervision to the prediction model and a curriculum learning strategy to stabilize the training of the model. Experiments on three public cooperative perception datasets demonstrate that the proposed method is effective in alleviating the impacts of communication interruption on cooperative perception.

翻译：协同感知通过V2X通信与相邻智能体交换信息，能够显著提升自动驾驶车辆的感知性能，突破单个车辆感知能力的局限。然而，现有研究大多假设智能体间存在理想通信，忽略了因V2X通信不完善导致的显著且常见的*中断问题*——协作智能体无法成功接收协作消息，从而导致协同感知失败，引发安全隐患。为在实际场景中充分实现协同感知的优势，我们提出面向V2X通信中断的鲁棒协同感知系统V2X-INCOP（V2X通信中断感知协同感知），该系统利用历史协作信息恢复因中断丢失的信息，从而缓解中断问题的影响。为实现全面恢复，我们设计了一种通信自适应多尺度时空预测模型，该模型能够根据V2X通信条件提取多尺度时空特征，并捕获对缺失信息预测最为关键的数据。为进一步提升恢复性能，我们采用知识蒸馏框架为预测模型提供显式且直接的监督，并引入课程学习策略以稳定模型训练过程。在三个公开协同感知数据集上的实验表明，所提方法能有效缓解通信中断对协同感知的影响。