A Study of State-of-the-Art DL Methods for Mixed Traffic Trajectory Prediction

In  recent  years,  with  the  increased availability of computational power and large-scale datasets, data-driving approaches, especially Deep Learning (DL) approaches, have  been  largely  used  for  trajectory modeling. The performance for pedestrian trajectory prediction in crowded spaces has been improved year by year, such as the state-of-the-art Social-LSTM (Alahi et al., 2016) CVAE (Lee et al., 2017), and Social-GAN (Gupta et al., 2018). The goal of this master thesis is to apply such stat-of-the-art DL approaches in a more challenging environment—shared space—for trajectory prediction with mixed traffic agents and compare their performance.