Motion Forecasting
66 papers with code • 1 benchmarks • 12 datasets
Motion forecasting is the task of predicting the location of a tracked object in the future
Benchmarks
These leaderboards are used to track progress in Motion Forecasting
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Datasets
Argoverse
Waymo Open Motion Dataset
Argoverse-HD
Argoverse 2 Motion Forecasting
SinD
Argoverse 2 Sensor
Lyft Level 5 Prediction
Argoverse 2 Lidar
Most implemented papers
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
abduallahmohamed/Social-STGCNN
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CVPR 2020
Better machine understanding of pedestrian behaviors enables faster progress in modeling interactions between agents such as autonomous vehicles and humans.
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
autonomousvision/transfuser
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CVPR 2021
How should representations from complementary sensors be integrated for autonomous driving?
DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets
Tsinghua-MARS-Lab/DenseTNT
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ICCV 2021
In this work, we propose an anchor-free and end-to-end trajectory prediction model, named DenseTNT, that directly outputs a set of trajectories from dense goal candidates.
Are socially-aware trajectory prediction models really socially-aware?
An attack is a small yet carefully-crafted perturbations to fail predictors.
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective
vita-epfl/causalmotion
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CVPR 2022
Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting.
MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction
stepankonev/waymo-motion-prediction-challenge-2022-multipath-plus-plus
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Predicting the future behavior of road users is one of the most challenging and important problems in autonomous driving.
HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction
To tackle this challenge, we propose Hierarchical Vector Transformer (HiVT) for fast and accurate multi-agent motion prediction.
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
zhejz/hptr
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In this paper, we present Wayformer, a family of attention based architectures for motion forecasting that are simple and homogeneous.
trajdata: A Unified Interface to Multiple Human Trajectory Datasets
nvlabs/trajdata
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NeurIPS 2023
The field of trajectory forecasting has grown significantly in recent years, partially owing to the release of numerous large-scale, real-world human trajectory datasets for autonomous vehicles (AVs) and pedestrian motion tracking.
Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders
jchengai/forecast-mae
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ICCV 2023
This study explores the application of self-supervised learning (SSL) to the task of motion forecasting, an area that has not yet been extensively investigated despite the widespread success of SSL in computer vision and natural language processing.
