Classification of Drivers Manoeuvre for Road Intersection Crossing with Synthetic and Real Data

Abstract : When approaching a road intersection, drivers consider several factors and choose amongst different likely manoeuvres. For an autonomous agent, it is fundamental to understand what other drivers are doing before deciding their own manoeuvres. These are seldom be the same as intersections differ and the situations too. Whilst, learning techniques can be used to process features of trajectories and to predict manoeuvres of others cars. The problem with such approaches is the difficult process of recording data for each intersection, not only of the subject vehicle but of the other vehicles. To address this problem, a hybrid data set was constructed. It is built in a simulated environment and completed with real data after has driven multiple times across the intersection. To analyze these data, classification technique is used to find the common range of features for each manoeuvre. Random forest classifiers are used in conjunction with our functional discretization to analyze the trajectories of cars approaching an intersection. The classifiers can determine the longitudinal manoeuvre as well as the direction. We show how our approach performs compared to other classifiers and space discretization. In addition, we demonstrate the impact and the usefulness of the mixture between simulated and real data. An improvement of 30% accuracy is obtained with the hybrid data set, and 5% using our functional discretization with respect to baseline approach.
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Mathieu Barbier, Christian Laugier, Olivier Simonin, Javier Ibanez-Guzman. Classification of Drivers Manoeuvre for Road Intersection Crossing with Synthetic and Real Data. 2017 IEEE intelligent Vehicles Symposium, IEEE Intelligent Transportation Systems Society, Jun 2017, Los Angeles, United States. pp.7. ⟨hal-01519709⟩

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