Cooperative perception enabled by V2X Communication technologies can significantly improve the perception performance of autonomous vehicles beyond the limited perception ability of the individual vehicles, therefore, improving the safety and efficiency of autonomous driving in intelligent transportation systems. However, in order to fully reap the benefits of cooperative perception in practice, the impacts of imperfect V2X communication, i.e., communication errors and disruptions, need to be understood and effective remedies need to be developed to alleviate their adverse impacts. Motivated by this need, we propose a novel INterruption-aware robust COoperative Perception (V2X-INCOP) solution for V2X communication-aided autonomous driving, which leverages historical information to recover missing information due to interruption. 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 direct supervision to the prediction model and a curriculum learning strategy to stabilize the training of the model. Our experiments on three public cooperative perception datasets demonstrate that our proposed method is effective in alleviating the impacts of communication interruption on cooperative perception.

翻译：V2X通信技术支持的协同感知能够显著提升自动驾驶车辆超越单车有限感知能力的感知性能，从而提高智能交通系统中自动驾驶的安全性与效率。然而，为在现实中充分实现协同感知的效益，需理解不完美V2X通信（即通信错误与中断）的影响，并开发有效缓解其负面影响的方案。基于此需求，我们提出一种面向V2X通信辅助自动驾驶的新型中断鲁棒协同感知（V2X-INCOP）方法，该方法利用历史信息恢复因通信中断缺失的信息。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，该模型基于V2X通信条件提取多尺度时空特征，并捕获用于缺失信息预测的最关键信息。为进一步提升恢复性能，我们采用知识蒸馏框架对预测模型进行直接监督，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。