On the Application of Machine Learning for Cut-in Maneuver Recognition in Platooning Scenarios

Cut-in into vehicle platoons is a dangerous driving maneuver that affects the safety and efficiency of platooning vehicles. An accurate prediction of such maneuver enables the platooning system to take safety measures that ensure the platoon safety and integrity. The contribution of this paper consists of an evaluation of a set of supervised machine learning algorithms for cut-in maneuver recognition, for eventual use in platooning systems. The models were trained and tested on a large-scale publicly available driving dataset, from which cut-in events were extracted. The results show that tree-based classifiers such as Gradient Booting Machine can recognize the cut-in maneuvers with an Fl-score of 98%. An experiment to investigate the model performance with advanced prediction times shows that up to 80.5% of the cut-ins were correctly predicted 1 second before the lane crossing time.

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