Trajectory Forecasting
70 papers with code • 4 benchmarks • 16 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)
Datasets
Most implemented papers
From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting
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.
AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting
Instead, we would prefer a method that allows an agent's state at one time to directly affect another agent's state at a future time.
CLIMAT: Clinically-Inspired Multi-Agent Transformers for Knee Osteoarthritis Trajectory Forecasting
We show the effectiveness of our method in predicting the development of structural knee osteoarthritis changes over time.
RedMotion: Motion Prediction via Redundancy Reduction
Predicting the future motion of traffic agents is vital for self-driving vehicles to ensure their safe operation.
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints
Whereas, the social attention component aggregates information across the different agent interactions and extracts the most important trajectory information from the surrounding neighbors.
The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs
Developing safe human-robot interaction systems is a necessary step towards the widespread integration of autonomous agents in society.
Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs
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.
Forecasting Pedestrian Trajectory with Machine-Annotated Training Data
In this work, we present a deep learning approach for pedestrian trajectory forecasting using a single vehicle-mounted camera.
Real Time Trajectory Prediction Using Deep Conditional Generative Models
Our method uses encoder and decoder deep networks that maps complete or partial trajectories to a Gaussian distributed latent space and back, allowing for fast inference of the future values of a trajectory given previous observations.