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Trajectory prediction is an important task, especially in autonomous driving.The ability to forecast the position of other moving agents can yield to aneffective planning, ensuring safety for the autonomous vehicle as well for theobserved entities. In this work we propose a data driven approach based onMarkov Chains to generate synthetic trajectories, which are useful for traininga multiple future trajectory predictor. The advantages are twofold: on the onehand synthetic samples can be used to augment existing datasets and train moreeffective predictors; on the other hand, it allows to generate samples withmultiple ground truths, corresponding to diverse equally likely outcomes of theobserved trajectory. We define a trajectory prediction model and a loss thatexplicitly address the multimodality of the problem and we show that combiningsynthetic and real data leads to prediction improvements, obtaining state ofthe art results.

Multiple Future Prediction Leveraging Synthetic Trajectories