Trajectory Forecasting

45 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)

Most implemented papers

Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

agrimgupta92/sgan 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 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 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 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 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 CVPR 2020

Better machine understanding of pedestrian behaviors enables faster progress in modeling interactions between agents such as autonomous vehicles and humans.

Exploring Dynamic Context for Multi-path Trajectory Prediction

wtliao/DCENet 30 Oct 2020

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 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

lpaparusso/drive-forecast 5 Mar 2021

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.

SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints

coolsunxu/sophie CVPR 2019

Whereas, the social attention component aggregates information across the different agent interactions and extracts the most important trajectory information from the surrounding neighbors.