Forecasting Human Trajectories with Uncertainty Estimation

Abstract

Human trajectory forecasting is a task which has been getting increasingly more attention in recent years. It is often used in robotics research as autonomous robots have to be well aware of the movement patterns of surrounding pedestrians to ensure safe and collision-free navigation. Many recent trajectory prediction works have been focused on neural network based solutions which need to be trained on large amounts of data. We propose a new generative trajectory forecasting method which does not need to be previously trained and is algorithmically simple and intuitive. Our method produces a multi-modal output to convey the uncertainty in human motion and is configurable with a set of parameters to adapt it to various environments. We show that our method performs nearly as good and in some cases better than state-of-the-art forecasting models when considering the task of predicting trajectories in an unseen environment. The results indicate that when deploying a forecasting model in an environment for which there is not a lot of data available, a neural network can be rivaled by a simpler approach.