Search Results for author:
Found 11 papers, 2 papers with code
RMP: A Random Mask Pretrain Framework for Motion Prediction
As the pretraining technique is growing in popularity, little work has been done on pretrained learning-based motion prediction methods in autonomous driving.
TSGN: Temporal Scene Graph Neural Networks with Projected Vectorized Representation for Multi-Agent Motion Prediction
Meanwhile, we propose a Hierarchical Lane Transformer for capturing interactions between agents and road network, which filters the surrounding road network and only keeps the most probable lane segments which could have an impact on the future behavior of the target agent.
Exploiting map information for self-supervised learning in motion forecasting
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.
Uncertainty estimation for Cross-dataset performance in Trajectory prediction
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.
ImPosing: Implicit Pose Encoding for Efficient Visual Localization
We propose a novel learning-based formulation for visual localization of vehicles that can operate in real-time in city-scale environments.
Enhanced Behavioral Cloning with Environmental Losses for Self-Driving Vehicles
The explanability study suggests that the benefits obtained are associated with a higher relevance of non-drivable areas in the agent's decisions compared to classical behavioral cloning.
Information Extraction from Visually Rich Documents with Font Style Embeddings
Information extraction (IE) from documents is an intensive area of research with a large set of industrial applications.
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
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
GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation
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)
HOME: Heatmap Output for future Motion Estimation
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
Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecasting
This paper presents a novel vehicle motion forecasting method based on multi-head attention.
