Multi-future Trajectory Prediction
9 papers with code • 5 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Multi-future Trajectory Prediction
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ETH
JAAD
PIE
TrajAir: A General Aviation Trajectory Dataset
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.
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.
Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs
amiryanj/socialways
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CVPR 2019
We show through experiments on real and synthetic data that the proposed method leads to generate more diverse samples and to preserve the modes of the predictive distribution.
The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction
JunweiLiang/Multiverse
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CVPR 2020
The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals.
AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction
Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications.
DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting
Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an autonomous agent has to navigate inside a human-centric environment.
BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation
umautobots/bidireaction-trajectory-prediction
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BiTraP estimates the goal (end-point) of trajectories and introduces a novel bi-directional decoder to improve longer-term trajectory prediction accuracy.
Stepwise Goal-Driven Networks for Trajectory Prediction
We propose to predict the future trajectories of observed agents (e. g., pedestrians or vehicles) by estimating and using their goals at multiple time scales.
Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation
abduallahmohamed/social-implicit
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AMD is a metric that quantifies how close the whole generated samples are to the ground truth.
