Pedestrian Trajectory Prediction is the task of predicting a set of possible future trajectories for a given observation. This ability is indispensable for any autonomous platform navigating through crowded scenes and interacting with humans. In recent years, many research papers have leveraged the power of gernative models in order to predict a set of possible future trajectories.
In my research I particulary study multimodality of trajectory predictions. In particualr, I am interested in building models that predict a set of divers but still realistic trajectories.
In our ACCV 20 oral paper "Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation" we build a model that learns a discrete probaility map of future intermediate goal positions. These goal positions are used to pre-condition the decoder to generate the final trajectory.
For more information about Goal-GAN check out the links below:
Next to my main research I constantly supervise students for a guided research project or their theses, especially in the field of pedestrian trajectory prediction. If you are a student from TU Munich and motivated in doing a research project in Pedin that field please contact me via mail.