Online V2X Scheduling for Raw-Level Cooperative Perception


SOURCE IEEE ICC’22,Seoul, South Korea, May 16-20, 2022

Published Date:2022-05


Cooperative perception of connected vehicles comes to the rescue when the field of view restricts stand-alone intelligence. While raw-level cooperative perception preserves most information to guarantee the accuracy, it is demanding in communication bandwidth and computation power. Therefore, it is important to schedule the sensor data of the most beneficial vehicle with complementary view and stable network connection. In this paper, we present a model of raw-level cooperative perception and formulate the energy minimization problem of sensor sharing scheduling as a variant of the Multi-Armed Bandit (MAB) problem. Specifically, the volatility of the neighboring vehicles, the heterogeneity of V2X channels, and the time-varying traffic context are taken into consideration. Then we propose an online learning-based algorithm with logarithmic performance loss, achieving a decent trade-off between exploration and exploitation. Simulation results under stationary and dynamic scenarios indicate that the proposed algorithm quickly learns to schedule the optimal cooperative vehicle and saves more energy as compared to baseline algorithms.


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