Ongoing projects
![](https://www.epfl.ch/labs/vita/wp-content/uploads/2024/04/Screenshot-2024-04-29-at-16.56.53-2-384x216.png)
Social-Transmotion
We translate the idea of a prompt from Natural Language Processing (NLP) to the task of human trajectory prediction, where a prompt can be a sequence of x-y coordinates on the ground, bounding boxes or body poses.
![](https://www.epfl.ch/labs/vita/wp-content/uploads/2024/04/unposed-383x216.gif)
Human Pose Forecasting
First, we develop an open-source library for human pose forecasting. Next, we model the uncertainty in the task, and then propose a denoising diffusion model to handle noisy inputs.
![](https://www.epfl.ch/labs/vita/wp-content/uploads/2024/04/unitraj-384x216.gif)
Vehicle Trajectory Prediction
Predicting how the future will unroll is essential for a self-driving car system. We propose generalizable prediction models for this regard. The predictions are then employed in the planning pillar or for risk assessment.
![](https://www.epfl.ch/labs/vita/wp-content/uploads/2021/10/pull-sattack-384x216.png)
Robust Trajectory Prediction
How robust are the trajectory prediction models? We study this question from different perspectives on vehicle and pedestrian trajectory prediction models.
![](https://www.epfl.ch/labs/vita/wp-content/uploads/2021/08/snce-384x216.png)
Social-NCE
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. In this work, we introduce a contrastive learning approach built with negative data augment to promote robust motion representations.
![](https://www.epfl.ch/labs/vita/wp-content/uploads/2024/04/PullFigure-384x216.jpg)
TrajNet++: Socially-Aware Human Trajectory Forecasting
We present an in-depth analysis of existing deep learning based methods for modelling social interactions in crowds. To objectively compare the performance of these interaction-based forecasting models, we develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
![](https://www.epfl.ch/labs/vita/wp-content/uploads/2022/02/stopandgo-384x216.jpg)
Pedestrian Stop and Go
Predicting whether pedestrians will stop walking (Stop) or start to walk (Go) in the near future, for better trajectory prediction around road traffic.
![](https://www.epfl.ch/labs/vita/wp-content/uploads/2022/06/bb_pred-384x216.gif)
Pedestrian Bounding Box Prediction
A libary for predicting 2D and 3D bounding boxes of humans in autonomous driving scenarios