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 GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
agrimgupta92/sgan
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CVPR 2018
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
StanfordASL/Trajectron-plus-plus
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ECCV 2020
Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation.
TNT: Target-driveN Trajectory Prediction
henry1iu/tnt-trajectory-prediction
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Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captured by a set of target states.
Social NCE: Contrastive Learning of Socially-aware Motion Representations
vita-epfl/social-nce
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ICCV 2021
Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds.
Argoverse: 3D Tracking and Forecasting with Rich Maps
In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting.
One Thousand and One Hours: Self-driving Motion Prediction Dataset
wenkaip-1836890/pyromid_l5prediction
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Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1, 000 hours of data.
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
DerrickXuNu/OpenCOOD
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ECCV 2020
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles.
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
autonomousvision/transfuser
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At the time of submission, TransFuser outperforms all prior work on the CARLA leaderboard in terms of driving score by a large margin.
Peeking into the Future: Predicting Future Person Activities and Locations in Videos
google/next-prediction
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CVPR 2019
To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen.
Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging
Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient.
