Multiple UAVs collaborative traffic monitoring with intention-based communication

Abstract

This article proposes an innovative ground traffic management approach that utilizes the mobility, flexibility, and collaborative capabilities of multiple Unmanned Aerial Vehicles (UAVs). The objective is to enhance the navigation and driving experience of self-driving vehicles by employing UAVs to cover blind areas that cannot be observed by ground monitoring equipment, thereby facilitating the avoidance of congested routes. This study focuses on determining the optimal number of UAVs and enhancing the scheduling strategy and task assignment method by introducing novel UAV communication collaboration techniques. The research faces challenges such as a limited quantity of UAVs, rapid response and feedback, variations in traffic conditions and tasks, and system complexity. Furthermore, the difficulty of communication collaboration is exacerbated by the incomplete connectivity of UAV networks. To address these challenges, this article introduces a method that utilizes intention information as the message for communication between UAVs, thereby enhancing their collaborative capabilities. Using the Multi-Agent Reinforcement Learning (MARL) neural network, this method generates intention information and utilizes the intention information generated by other UAVs to optimize the flight control and traffic monitoring task allocation strategy. This method effectively addresses the challenge of UAV collaboration in efficiently managing a large volume of stochastic traffic monitoring tasks. Experimental results demonstrate that the proposed approach significantly improves vehicle navigation accuracy and achieves a more balanced distribution of workload among multiple UAVs. Moreover, by sharing road conditions detected in the cloud, it reduces operational costs and opens up possibilities for the practical implementation of UAVs in traffic management.