A Trajectory Prediction Method Based on Social Forces, Scene Information and Motion Habit

Foreseeing the positions of the surrounding objects is a critical technique to autonomous vehicles. Referring to the positions predicted by the trajectories, moving vehicles could avoid possible collisions. Many trajectory prediction methods about have been proposed, from traditional hand-crafted approaches to Recurrent Neural Network (RNN) models. Although the proposed methods can achieve satisfactory results on publicly available human-trajectory datasets, there are still exist some problems, such as predicting positions in obstacles, sharp turns that are not socially acceptable. We adopted the concepts of social GAN to propose a new model that take the social forces, scene information and motion direction into account when predicting trajectories. We demonstrate the performance of our proposed methods by adopting several videos from Stanford Drone Dataset.