Online and Adaptive Parking Availability Mapping: An Uncertainty-Aware Active Sensing Approach for Connected Vehicles

Research on connected vehicles represents a continuously evolving technological domain, fostered by the emerging Internet of Things paradigm and the recent advances in intelligent transportation systems. In the context of assisted driving, connected vehicle technology provides real-time information about the surrounding traffic conditions. In this regard, 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 lower performance in terms of mapping convergence speed and adaptation capabilities.