IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction

Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians around is beneficial for policy decision to avoid collision. It is a challenging issue because humans have different walking motions and the interactions between humans and objects in the current environment, especially between human themselves, are complex. Previous researches have focused on how to model the human-human interactions, however, neglecting the relative importance of interactions. In order to address this issue, we introduce a novel mechanism based on the correntropy, which not only can measure the relative importance of human-human interactions, but also can build personal space for each pedestrian. We further propose an Interaction Module including this data-driven mechanism that can effectively extract feature representations of dynamic human-human interactions in the scene and calculate corresponding weights to represent the importance of different interactions. To share such social messages among pedestrians, we design an interaction-aware architecture based on the Long Short-Term Memory (LSTM) network for trajectory prediction. We demonstrate the performance of our model on two public datasets and the experimental results demonstrate that our model can achieve better performance than several latest methods with good performance.