59 citations · 71 across the 9 of their papers we have counts for
11 papers
RMP: A Random Mask Pretrain Framework for Motion Prediction
Yi Yang, Qingwen Zhang, Thomas Gilles +2
As the pretraining technique is growing in popularity, little work has been done on pretrained learning-based motion prediction methods in autonomous driving. In this paper, we pro…
MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly
Raphael Chekroun, Thomas Gilles, Marin Toromanoff +2
We present MBAPPE, a novel approach to motion planning for autonomous driving combining tree search with a partially-learned model of the environment. Leveraging the inherent expla…
TSGN: Temporal Scene Graph Neural Networks with Projected Vectorized Representation for Multi-Agent Motion Prediction
Yunong Wu, Thomas Gilles, Bogdan Stanciulescu +1
Predicting future motions of nearby agents is essential for an autonomous vehicle to take safe and effective actions. In this paper, we propose TSGN, a framework using Temporal Sce…
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…
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…
ImPosing: Implicit Pose Encoding for Efficient Visual Localization
Arthur Moreau, Thomas Gilles, Nathan Piasco +3
We propose a novel learning-based formulation for visual localization of vehicles that can operate in real-time in city-scale environments. Visual localization algorithms determine…
Enhanced Behavioral Cloning with Environmental Losses for Self-Driving Vehicles
Nelson Fernandez Pinto, Thomas Gilles
Learned path planners have attracted research interest due to their ability to model human driving behavior and rapid inference. Recent works on behavioral cloning show that simple…
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…
Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecasting
Jean Mercat, Thomas Gilles, Nicole El Zoghby +3
This paper presents a novel vehicle motion forecasting method based on multi-head attention. It produces joint forecasts for all vehicles on a road scene as sequences of multi-moda…