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Cooperative perception enabled by V2X Communication technologies cansignificantly improve the perception performance of autonomous vehicles beyondthe limited perception ability of the individual vehicles, therefore, improvingthe safety and efficiency of autonomous driving in intelligent transportationsystems. However, in order to fully reap the benefits of cooperative perceptionin practice, the impacts of imperfect V2X communication, i.e., communicationerrors and disruptions, need to be understood and effective remedies need to bedeveloped to alleviate their adverse impacts. Motivated by this need, wepropose a novel INterruption-aware robust COoperative Perception (V2X-INCOP)solution for V2X communication-aided autonomous driving, which leverageshistorical information to recover missing information due to interruption. Toachieve comprehensive recovery, we design a communication adaptive multi-scalespatial-temporal prediction model to extract multi-scale spatial-temporalfeatures based on V2X communication conditions and capture the most significantinformation for the prediction of the missing information. To further improverecovery performance, we adopt a knowledge distillation framework to givedirect supervision to the prediction model and a curriculum learning strategyto stabilize the training of the model. Our experiments on three publiccooperative perception datasets demonstrate that our proposed method iseffective in alleviating the impacts of communication interruption oncooperative perception.

Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving