Digital Twin Model Development for Autonomous Tram Localization

The rapid advancement of information technologies led to the rapid growth of various aspects, one of which is autonomous vehicles. Digital twin technology is being frequently developed in autonomous vehicle research, enabling real-time remote monitoring and control of the vehicle’s physical assets. This technology can reduce maintenance costs and risks as well as prevent and speed up accident management. This paper proposes a digital twin model for the autonomous tram, one of the vehicles widely explored due to its safety, low emissions, and high capacity. In this research, the proposed digital twin model is utilized to virtually represent the kinematics of the tram prototype in a 2D model from data sent via Message Queuing Telemetry Transport (MQTT) protocol, enabling real- time remote control with low-band consumption. Virtual representation of the tram prototype is gathered via physical sensors and Long Short-Term Memory (LSTM) as the virtual model and controlled by a Stanley controller. The results confirmed that the use of the proposed digital twin model could remotely monitor and control the autonomous tram prototype in real-time conditions.