no code implementations • 10 Oct 2022 • Caio Azevedo, Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou
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 information such as graph connectivity with the intent of improving map comprehension and generalization.
no code implementations • 9 Aug 2022 • Yann Koeberle, Stefano Sabatini, Dzmitry Tsishkou, Christophe Sabourin
In this work, we show that a trade-off exists between imitating human driving and maintaining safety when learning driving policies.
no code implementations • 15 May 2022 • Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, Fabien Moutarde
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 far on the generalizability and the transferability of these methods across dataset.
no code implementations • ICLR 2022 • Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, Fabien Moutarde
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.
Ranked #6 on Trajectory Prediction on nuScenes
no code implementations • 4 Sep 2021 • Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, Fabien Moutarde
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 position probability distribution for a given agent in a traffic scene.
Ranked #1 on Trajectory Prediction on INTERACTION Dataset - Validation (minFDE6 metric)
1 code implementation • 23 May 2021 • Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, Fabien Moutarde
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.
Ranked #32 on Motion Forecasting on Argoverse CVPR 2020