Autonomous vehicles are expected to drive in complex scenarios with several independent non cooperating agents. Path planning for safely navigating in such environments can not just rely on perceiving present location and motion of other agents. It requires instead to predict such variables in a far enough future. In this paper we address the problem of multimodal trajectory prediction exploiting a Memory Augmented Neural Network. Our method learns past and future trajectory embeddings using recurrent neural networks and exploits an associative external memory to store and retrieve such embeddings. Trajectory prediction is then performed by decoding in-memory future encodings conditioned with the observed past. We incorporate scene knowledge in the decoding state by learning a CNN on top of semantic scene maps. Memory growth is limited by learning a writing controller based on the predictive capability of existing embeddings. We show that our method is able to natively perform multi-modal trajectory prediction obtaining state-of-the art results on three datasets. Moreover, thanks to the non-parametric nature of the memory module, we show how once trained our system can continuously improve by ingesting novel patterns.

Mantra: Memory augmented networks for multiple trajectory prediction / Francesco Marchetti, Federico Becattini, Lorenzo Seidenari, Alberto Del Bimbo. - ELETTRONICO. - (2020), pp. 7141-7150. (Intervento presentato al convegno IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020) [10.1109/CVPR42600.2020.00717].

Mantra: Memory augmented networks for multiple trajectory prediction

Francesco Marchetti;Federico Becattini;Lorenzo Seidenari;Alberto Del Bimbo
2020

Abstract

Autonomous vehicles are expected to drive in complex scenarios with several independent non cooperating agents. Path planning for safely navigating in such environments can not just rely on perceiving present location and motion of other agents. It requires instead to predict such variables in a far enough future. In this paper we address the problem of multimodal trajectory prediction exploiting a Memory Augmented Neural Network. Our method learns past and future trajectory embeddings using recurrent neural networks and exploits an associative external memory to store and retrieve such embeddings. Trajectory prediction is then performed by decoding in-memory future encodings conditioned with the observed past. We incorporate scene knowledge in the decoding state by learning a CNN on top of semantic scene maps. Memory growth is limited by learning a writing controller based on the predictive capability of existing embeddings. We show that our method is able to natively perform multi-modal trajectory prediction obtaining state-of-the art results on three datasets. Moreover, thanks to the non-parametric nature of the memory module, we show how once trained our system can continuously improve by ingesting novel patterns.
2020
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020
Goal 9: Industry, Innovation, and Infrastructure
Goal 11: Sustainable cities and communities
Francesco Marchetti, Federico Becattini, Lorenzo Seidenari, Alberto Del Bimbo
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1206703
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