Adaptive traffic light prediction via Kalman filtering

Current fields of research in the automotive sector are dealing with the development of new driving-assistance-functions that aim to improve security, efficiency and comfort of vehicles. A significant field of study represents the prediction of traffic signals ahead that enable innovative functionalities such as Green Light Optimal Speed Advisory (GLOSA) or efficient start-stop control. This paper deals with the challenges of predicting future signals of traffic-adaptive traffic lights. First of all, we extract important characteristics of adaptive traffic lights and the underlying traffic situation at crossings relying on historical data of several Munich traffic lights. Based on these insights, we present and evaluate a generic model to predict future traffic-adaptive traffic signals at crossings. We show that with the proposed model, 95% of future signals can be predicted with an accuracy of 95% at best. On average, 71% of future signals can be predicted with an accuracy of 95% for the considered traffic lights.