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Analyzing Vehicle Traces to Find and Exploit Correlated Traffic Lights for Efficient Driving

Author(s): Markus Kerper, Christian Wewetzer, Martin Mauve.
Title: Analyzing Vehicle Traces to Find and Exploit Correlated Traffic Lights for Efficient Driving
Published: IV '12: Proceedings of the IEEE Intelligent Vehicles Symposium, pp. --, Alcalá de Henares, Spain, June 2012
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Abstract: Traffic lights strongly impact vehicle movement and fuel consumptionin cities. If drivers were aware of the situation at arrival time,they could adapt their velocity and thus reduce the number ofunnecessary stops and fuel consumption.To predict the influence of the traffic light ahead on the velocity ofan approaching vehicle, our vision is that drivers share their vehicletraces in a digital cloud, and in return benefit from algorithmsevaluating the collected data.With Traffic Light Coordination Analysis (TLCorA), we present one suchalgorithm analyzing vehicle traces. When a vehicle is approaching atraffic light, TLCorA finds traces of vehicles similar to that of thevehicle at the previous traffic light, and calculates from theirapproach to the upcoming traffic light whether there is arepresentative approaching trace. For this purpose, TLCorA classifiesthe approaching traces with help of a clustering algorithm based ondynamic time warping.We implement TLCorA in simulations of different traffic lightsignalization algorithms, and study the calculated approachprobabilities depending on the respective traffic light correlationlevel in the scenarios.
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