Enhancing vehicular safety with multi-object multi-camera tracking in Open RAN networks

• Distributed Multi-Camera Multi-Object tracking system is proposed. • Tracking must deal with network latency and inherent asynchronism. • The edge service is leveraged using Open RAN 6G technology. • An automatic hazard metric is proposed for vehicular applications. • Impact of network latency on tracking is studied experimentally. Hazard detection is an important problem for Intelligent Transportation Systems (ITS), although, developing and deployment of such systems is a complicated task, given human presence in the loop and a large account of non-controlled variables (e.g. traffic levels, weather conditions, etc). In addition, a distributed hazard detection system will require a communication network for a large number of sensors, making this network a crucial element of the system and a potential bottleneck for its performance. Therefore, a suitable platform is needed for the development and validation of vehicular applications that handle both the vehicular and network aspects of the system to seamlessly migrate an application to an urban environment. This paper addresses these gaps by designing a hardware-in-the-loop architecture for a realistic ITS hazard detection system. The platform relies on CARLA simulator to provide a safe, controlled, and repeatable environment to test the application, and a software-defined Open RAN network that allows studying the effect of communications nuisances on the service and the possibility to implement network aware services to enhance performance. In addition, a hazard detection service is devised using the described architecture. Our approach uses a combination of machine learning detections with Kalman (or particle) filters to aggregate data from multiple cameras. We assume detections are asynchronous and data transmission should be minimized to ensure scalability. A data association criterion based on Mahalonabis distance is proposed to automatically associate filters with trajectories. Our experiments provide insights into the throughput and latency of the network and their impact on the ITS service. The results show the service is robust to end-to-end latency, making comparisons among KFs, PFs and unscented KFs.