Semi-Supervised and Multi-Task Learning for On-Street Parking Space Status Inference

To manage on-street parking spaces, magnetic sensor is often used due to its low cost and flexibility in installation and usage. However, its signals are easily affected by environment, vehicle type, installation location and moving neighboring vehicles. Besides, accidental installation also leads to non-unified coordinate of magnetic sensors which makes the management system difficult to recognize. To overcome these challenges, we proposed a novel semi-supervised and multi-task learning framework for sensor based on-street parking slot inference with three contributions. First, a Coordinate Transform Module is integrated into our framework to reduce the diversity of input signals by transforming them adaptively into a unified coordinate. Second, to learn the generalized and discriminative features while minimizing the amount of labeled data, we introduce a Multi-task Module to leverage the information from both labeled and unlabeled data. Third, we embed a Temporal Module, which observes and memorizes the parking states from time to time, to infer parking space status in a reliable way. The experimental results show that, with the proposed three modules, our end-to-end training framework could reduce the error detection and hence improve the system accuracy.