UAV assisted smart parking solution

Smart parking solutions for big cities are critically important for reducing traffic congestion and vehicle energy consumption in big cities. Visual-based detection methods have been proposed since their maintenance costs are typically lower. However, visual based detection methods in existing systems are not robust due to varying light intensities, occlusions, and their deployment costs can still be expensive. In this paper, we present a collaborative UAV aided smart parking solution where a small team of low cost UAVs are used to collaboratively identify free parking spots of parking lots within campuses. We design a novel navigation control scheme to allow such UAVs to avoid obstacles and cover the parking lots sufficiently. In addition, we also present a visual detection scheme using the generative adversarial network (GAN) which allows us to predict parking availability without requiring pre-identification of parking spots. Simulation results indicate that our approach show promising results.