Crowdedness of buses is playing an increasingly important role in the disease control of COVID-19. The lack of a practical approach to sensing the crowdedness of buses is a major problem. This paper proposes a bus crowdedness sensing system which exploits deep learning-based object detection to count the numbers of passengers getting on and off a bus and thus estimate the crowdedness of buses in real time. In our prototype system, we combine YOLOv5s object detection model with Kalman Filter object tracking algorithm to implement a sensing algorithm running on a Jetson nano-based vehicular device mounted on a bus. By using the driving recorder video data taken from real bus, we experimentally evaluate the performance of the proposed sensing system to verify that our proposed system system improves counting accuracy and achieves real-time processing at the Jetson Nano platform.

As the capabilities and costs of Artificial Intelligence (AI) and of sensors (IoT) continue to improve, the concept of a “control system” can evolve beyond the operation of a discrete technical system based on numerical information and enter the realm of large-scale systems with both technical and social characteristics based on both numerical and unstructured information. This evolution has particular significance for applying the principles of Autonomous Decentralised Systems (ADS) [1]. This article considers the possible roles for ADS in complex technical and social systems extending up to global scales.