Personalized Trajectory Prediction via Distribution Discrimination

Trajectory prediction is confronted with the dilemma to capture the multi-modal nature of future dynamics with both diversity and accuracy. In this paper, we propose a distribution discrimination method (DisDis) to predict personalized motion pattern by distinguishing the potential distributions in a self-supervised manner. The key motivation of DisDis is the observation that the motion pattern of each person is personalized due to his/her habit, character, or goal. Specifically, we learn the latent distribution to represent different motion patterns and optimize it by contrastive discrimination. The contrastive distribution discrimination encourages latent distributions to be discriminative. Our method could be seamlessly integrated with existing multi-modal stochastic predictive models as a plug-and-play module to learn the more discriminative latent distribution. To evaluate the latent distribution, we further propose a new metric, probability cumulative minimum distance (PCMD) curve, which cumulatively calculates the minimum distance on the sorted probabilities. Experimental results on the ETH and UCY datasets show the effectiveness of our method.