Infrastructure Based Detection, Tracking and Modelling of Traffic Participants for Realistic Digital Twin Representation and Behavior Prediction

Traffic congestion and inefficiencies in urban mobility remain significant challenges, necessitating advanced solutions that leverage real-world data and intelligent control strategies. In order to optimize traffic flows e.g. through traffic light control, analysis of real-world scenarios in urban hotspots is required. This research integrates data of real traffic participants and traffic lights status recorded in the Test Field Autonomous Driving Baden-Wurttemberg (TAF-BW) into a digital twin. The focus lies on object detection and tracking in the test field, integration of this data into a digital twin and accurate modelling of pedestrians. The real and simulated data builds the framework for training of behavioral models of various traffic participants. Based on the intention of these participants, light signal control is possible to optimize traffic flow.