HOME: Heatmap Output for future Motion Estimation

In this paper, we propose HOME, a framework tackling the motion forecasting problem with an image output representing the probability distribution of the agent's future location. This method allows for a simple architecture with classic convolution networks coupled with attention mechanism for...

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Vydané v:2021 IEEE International Intelligent Transportation Systems Conference (ITSC) s. 500 - 507
Hlavní autori: Gilles, Thomas, Sabatini, Stefano, Tsishkou, Dzmitry, Stanciulescu, Bogdan, Moutarde, Fabien
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 19.09.2021
Predmet:
Benchmark testing
Heating systems
Motion estimation
Probability distribution
Sampling methods
Trajectory
Uncertainty
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Abstract In this paper, we propose HOME, a framework tackling the motion forecasting problem with an image output representing the probability distribution of the agent's future location. This method allows for a simple architecture with classic convolution networks coupled with attention mechanism for agent interactions, and outputs an unconstrained 2D top-view representation of the agent's possible future. Based on this output, we design two methods to sample a finite set of agent's future locations. These methods allow us to control the optimization trade-off between miss rate and final displacement error for multiple modalities without having to retrain any part of the model. We apply our method to the Argoverse Motion Forecasting Benchmark and achieve 1 st place on the online leaderboard.
AbstractList In this paper, we propose HOME, a framework tackling the motion forecasting problem with an image output representing the probability distribution of the agent's future location. This method allows for a simple architecture with classic convolution networks coupled with attention mechanism for agent interactions, and outputs an unconstrained 2D top-view representation of the agent's possible future. Based on this output, we design two methods to sample a finite set of agent's future locations. These methods allow us to control the optimization trade-off between miss rate and final displacement error for multiple modalities without having to retrain any part of the model. We apply our method to the Argoverse Motion Forecasting Benchmark and achieve 1 st place on the online leaderboard.
Author Sabatini, Stefano
Tsishkou, Dzmitry
Moutarde, Fabien
Stanciulescu, Bogdan
Gilles, Thomas
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  givenname: Thomas
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  organization: IoV team, Paris Research Center, Huawei Technologies,France
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  givenname: Stefano
  surname: Sabatini
  fullname: Sabatini, Stefano
  organization: IoV team, Paris Research Center, Huawei Technologies,France
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  organization: IoV team, Paris Research Center, Huawei Technologies,France
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  givenname: Bogdan
  surname: Stanciulescu
  fullname: Stanciulescu, Bogdan
  organization: PSL University,MINES ParisTech, Center for robotics
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  givenname: Fabien
  surname: Moutarde
  fullname: Moutarde, Fabien
  organization: PSL University,MINES ParisTech, Center for robotics
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SubjectTerms Benchmark testing
Heating systems
Motion estimation
Probability distribution
Sampling methods
Trajectory
Uncertainty
Title HOME: Heatmap Output for future Motion Estimation
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