  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 accuracy, it is demanding in
communication bandwidth and computation power. Therefore, it is important to
schedule the most beneficial vehicle to share its sensor in terms of
supplementary 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, volatility of the neighboring vehicles,
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 different scenarios indicate that
the proposed algorithm quickly learns to schedule the optimal cooperative
vehicle and saves more energy as compared to baseline algorithms.
