Probabilistic Trajectory Prediction for Urban Driving
A. Mészáros (TU Delft - Learning & Autonomous Control)
J. Kober – Promotor (University of Stuttgart, TU Delft - Learning & Autonomous Control)
Javier Alonso-Mora – Promotor (TU Delft - Learning & Autonomous Control)
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Abstract
Driving in urban environments is a challenging task, even for us humans. There is a variety of different traffic participants including other drivers, cyclists and pedestrians who each have their own unique behaviors. In order to navigate around them safely it is not enough to have detected their presence. We also need to reason about what they might do. However, we cannot always be certain of others’ course of action as we do not know which route they are taking, how risk-averse they are or how aware they are of their surroundings, along with a number of other psychological factors. Hence, we also need to reason how likely it is that they will take a particular course of action. For autonomous vehicles to navigate in urban environments with other traffic participants, we need to imbue them with the capacity to reason about what other participants may do. The main goal of this thesis is to provide contributions in the development of probabilistic trajectory prediction models which accurately capture the distribution over possible future trajectories of other traffic participants.....