MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving

Stepan Konev, Kirill Brodt, Artsiom Sanakoyeu
6/5/2022
cs.CV

Official code: stepankonev/MotionCNN-Waymo-Open-Motion-Dataset (39 stars, PyTorch, last commit 2023-04-22). 1 more implementation below.

Abstract

To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of other agents around it. Motion prediction is an extremely challenging task that recently gained significant attention within the research community. In this work, we present a simple and yet very strong baseline for multimodal motion prediction based purely on Convolutional Neural Networks. While being easy-to-implement, the proposed approach achieves competitive performance compared to the state-of-the-art methods and ranks 3rd on the 2021 Waymo Open Dataset Motion Prediction Challenge. Our source code is publicly available at GitHub

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Code Implementations(2)

stepankonev/MotionCNN-Waymo-Open-Motion-DatasetOfficial100%
PyTorch

Implementation of "MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving" for Waymo Open Motion Dataset

398PythonApr 22, 20233 years agoNOASSERTION
autonomous-drivingcvpr2021motion-predictionpytorchpytorch-implementation+5 more
kbrodt/waymo-motion-prediction-2021Community100%

Waymo: Motion Prediction 2021

19446PythonJun 19, 20213 years ago

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Cite this paper

@article{konev2022motioncnn,
  title  = {MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving},
  author = {Stepan Konev and Kirill Brodt and Artsiom Sanakoyeu},
  year   = {2022},
  eprint = {2206.02163},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2206.02163}
}

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