Pedestrian trajectory prediction with Convolutional Neural Networks

Abstract: Modelling the behaviour of pedestrians is essential in autonomous driving because consequences for misjudging the intentions of a pedestrian can be severe when dealing with vehicles. Therefore, for an autonomous vehicle to plan a safe and collision-free path, it is necessary not only to know the current position of nearby pedestrians but also their future trajectory. In literature, methods to approach the problem of pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. This thesis proposes a new approach to pedestrian trajectory prediction, with the introduction of a convolutional model. This new model is able to outperform recurrent models, and it achieves state-of-the-art results on the ETHUCY dataset and on the TrajNet dataset. Moreover, this thesis presents an effective system to represent pedestrian positions and powerful data augmentation techniques, such as the addition of noise and the use of random rotations, which can be applied to any model. Finally, a study on the effectiveness of various techniques to include social information is presented, which demonstrates that simpler approaches fail to capture complex social interaction.