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Applying map-masks to Trajectory Prediction for Interacting Traffic-Agents

Abstract : Autonomous vehicles perceive their surroundings and foresee the future behaviour of all other relevant interacting traffic-agents in order to navigate safely and to operate in the public road networks. Trajectory prediction is difficult due to the stochastic manner these different types of traffic-agents interact with each other and the changing navigation context. In this work, we propose a recurrent artificial neural network LSTM encoder-decoder based architecture to predict the movement of traffic agents. It uses map-masks of the area surrounding the ego vehicle and previous trajectory information to predict the trajectory of interacting traffic agents. This paper compares the proposed approach with LSTM baselines,using the NuScenes dataset which includes LiDAR point-cloud ground-truth data for traffic agents plus map information. Experimental results show that the proposed method outperforms the baselines based on the prediction accuracy.
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Contributor : Vyshakh Palli-Thazha <>
Submitted on : Wednesday, December 4, 2019 - 12:11:09 PM
Last modification on : Thursday, January 21, 2021 - 9:26:01 AM
Long-term archiving on: : Thursday, March 5, 2020 - 4:34:22 PM


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  • HAL Id : hal-02393250, version 1



Vyshakh Palli Thazha, David Filliat, Javier Ibañez-Guzmán. Applying map-masks to Trajectory Prediction for Interacting Traffic-Agents. 3rd Edition Deep Learning for Automated Driving (DLAD) workshop, IEEE International Conference on Intelligent Transportation Systems (ITSC'19), 2019. ⟨hal-02393250⟩



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