Deep learning based trajectory prediction for autonomous vehicles. (Prédiction de trajectoires pour les véhicules autonomes)

Kaouther Messaoud

Abstract: The trajectory prediction of neighboring agents of an autonomous vehicle is essential for autonomous driving in order to perform trajectory planning in an efficient manner. In this thesis, we tackle the problem of predicting the trajectory of a target vehicle in two different environments; a highway and an urban area (intersection, roundabout, etc.). To this end, we develop solutions based on deep machine learning by phasing the interactions between the target vehicle and the static and dynamic elements of the scene. In addition, in order to take into account the uncertainty of the future, we generate multiple plausible trajectories and the probability of occurrence of each. We also make sure that the predicted trajectories are realistic and conform to the structure of the scene. The solutions developed are evaluated using real driving datasets.