  Research on connected vehicles represents a continuously evolving
technological domain, fostered by the emerging Internet of Things (IoT)
paradigm and the recent advances in intelligent transportation systems.
Nowadays, vehicles are platforms capable of generating, receiving and
automatically act based on large amount of data. In the context of assisted
driving, connected vehicle technology provides real-time information about the
surrounding traffic conditions. Such information is expected to improve
drivers' quality of life, for example, by adopting decision making strategies
according to the current parking availability status. In this context, we
propose an online and adaptive scheme for parking availability mapping.
Specifically, we adopt an information-seeking active sensing approach to select
the incoming data, thus preserving the onboard storage and processing
resources; then, we estimate the parking availability through Gaussian Process
Regression. We compare the proposed algorithm with several baselines, which
attain inferior performance in terms of mapping convergence speed and
adaptivity capabilities; moreover, the proposed approach comes at the cost of a
very small computational demand.
