59 citations · 71 across the 5 of their papers we have counts for
6 papers
Exploiting map information for self-supervised learning in motion forecasting
Caio Azevedo, Thomas Gilles, Stefano Sabatini +1
Inspired by recent developments regarding the application of self-supervised learning (SSL), we devise an auxiliary task for trajectory prediction that takes advantage of map-only…
Exploring the trade off between human driving imitation and safety for traffic simulation
Yann Koeberle, Stefano Sabatini, Dzmitry Tsishkou +1
Traffic simulation has gained a lot of interest for quantitative evaluation of self driving vehicles performance. In order for a simulator to be a valuable test bench, it is requir…
Uncertainty estimation for Cross-dataset performance in Trajectory prediction
Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou +2
While a lot of work has been carried on developing trajectory prediction methods, and various datasets have been proposed for benchmarking this task, little study has been done so…
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou +2
In this paper, we propose THOMAS, a joint multi-agent trajectory prediction framework allowing for an efficient and consistent prediction of multi-agent multi-modal trajectories. W…
GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation
Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou +2
In this paper, we propose GOHOME, a method leveraging graph representations of the High Definition Map and sparse projections to generate a heatmap output representing the future p…
HOME: Heatmap Output for future Motion Estimation
Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou +2
In this paper, we propose HOME, a framework tackling the motion forecasting problem with an image output representing the probability distribution of the agent's future location. T…