Trajectory Forecasting
49 papers with code • 4 benchmarks • 9 datasets
Trajectory forecasting is a sequential prediction task, where a forecasting model predicts future trajectories of all moving agents (humans, vehicles, etc.) in a scene, based on their past trajectories and/or the scene context.
(Illustrative figure from Social NCE: Contrastive Learning of Socially-aware Motion Representations)
Benchmarks
These leaderboards are used to track progress in Trajectory Forecasting
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Datasets
UCY
JAAD
TrajNet
PIE
FPL
NBA SportVU
TrajAir: A General Aviation Trajectory Dataset
Euro-PVI
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.
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.
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
vincent-leguen/STDL
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NeurIPS 2019
We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization.
It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
HarshayuGirase/PECNet
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ECCV 2020
In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.
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.
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.
OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets
Human Trajectory Prediction (HTP) has gained much momentum in the last years and many solutions have been proposed to solve it.
Exploring Dynamic Context for Multi-path Trajectory Prediction
wtliao/DCENet
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In our framework, first, the spatial context between agents is explored by using self-attention architectures.
From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting
harshayugirase/human-path-prediction
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ICCV 2021
Uncertainty in future trajectories stems from two sources: (a) sources that are known to the agent but unknown to the model, such as long term goals and (b)sources that are unknown to both the agent & the model, such as intent of other agents & irreducible randomness indecisions.
Real-Time Forecasting of Driver-Vehicle Dynamics on 3D Roads: a Deep-Learning Framework Leveraging Bayesian Optimisation
This represents a particularly useful problem, for instance in autonomous driving, but it does not cover a spectrum of applications in control and simulation that require information on vehicle dynamics features other than pose and orientation.
