Modern traffic lights use information from induction loops and to some extend radar information. Recent developments in radar technology has made it possible to obtain more detailed information relevant to the control mechanism of the traffic light. Unfortunately much of the current controllers do not profit from this additional information. Using this information could minimize waiting times and energy waste. Uppaal Stratego is a tool that combines machine learning and model checking techniques to synthesize near optimal control strategies. The tool has been applied successfully to several case studies e.g. battery optimization in satellites, safe and optimal cruise control and optimal floor heating controlling. In this work we use Uppaal Stratego as an on-line controller for a signalised intersection. Our controller reads the current data from the radar sensors and effectively uses it to learn a near optimal controller at each control step. Our experiments report considerable reduction in the waiting times. Keywords: traffic lights, model checking, machine learning, optimization. 1 Introduction Traditionally, traffic signal control strategies are based on loop detectors embedded in the road surface. The loop detectors give a precise location of vehicles passing or occupying the loops, but the prediction of the vehicle dynamics is limited by the detector locations. The corresponding control strategies are discrete event-based with incremental extensions of green times supplemented with a number of specific decision rules. Despite the development of new computer based signal controllers there has been little development in the field of control strategies. With the most recent development in radar detection systems, radar detectors are feasible for road traffic detection. One radar detector, placed appropriately, can replace all the detector loops in one intersection approach. It can monitor the approach continuously and give a full account of all vehicles approaching the junction.
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