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

Multiple future prediction leveraging synthetic trajectories / Berlincioni L.; Becattini F.; Seidenari L.; Del Bimbo A.. - ELETTRONICO. - (2020), pp. 6081-6088. (Intervento presentato al convegno 25th International Conference on Pattern Recognition, ICPR 2020 tenutosi a ita nel 2021) [10.1109/ICPR48806.2021.9412158].

Multiple future prediction leveraging synthetic trajectories

Berlincioni L.;Becattini F.;Seidenari L.;Del Bimbo A.
2020

Abstract

Trajectory prediction is an important task, especially in autonomous driving. The ability to forecast the position of other moving agents can yield to an effective planning, ensuring safety for the autonomous vehicle as well for the observed entities. In this work we propose a data driven approach based on Markov Chains to generate synthetic trajectories, which are useful for training a multiple future trajectory predictor. The advantages are twofold: on the one hand synthetic samples can be used to augment existing datasets and train more effective predictors; on the other hand, it allows to generate samples with multiple ground truths, corresponding to diverse equally likely outcomes of the observed trajectory. We define a trajectory prediction model and a loss that explicitly address the multimodality of the problem and we show that combining synthetic and real data leads to prediction improvements, obtaining state of the art results.
2020
Proceedings - International Conference on Pattern Recognition
25th International Conference on Pattern Recognition, ICPR 2020
ita
2021
Berlincioni L.; Becattini F.; Seidenari L.; Del Bimbo A.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1245048
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