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HOME: Heatmap Output for future Motion Estimation

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arxiv 2105.10968 v2 pith:6CPRQREC submitted 2021-05-23 cs.CV cs.RO

HOME: Heatmap Output for future Motion Estimation

Thomas Gilles , Stefano Sabatini , Dzmitry Tsishkou , Bogdan Stanciulescu , Fabien Moutarde This is my paper
classification cs.CV cs.RO
keywords agentfuturemotionoutputforecastinghomemethodmethods
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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 1st place on the online leaderboard.

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  1. Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting

    cs.CV 2023-01 accept novelty 7.0

    Argoverse 2 introduces three new datasets with annotated sensor data, massive lidar collections, and challenging motion forecasting scenarios for autonomous driving research.