Ganet: Goal area network for motion forecasting
Predicting the future motion of road participants is crucial for autonomous driving but is
extremely challenging due to staggering motion uncertainty. Recently, most motion …
extremely challenging due to staggering motion uncertainty. Recently, most motion …
Trajectory prediction with graph-based dual-scale context fusion
Motion prediction for traffic participants is essential for a safe and robust automated driving
system, especially in cluttered urban environments. However, it is highly challenging due to …
system, especially in cluttered urban environments. However, it is highly challenging due to …
Lanercnn: Distributed representations for graph-centric motion forecasting
Forecasting the future behaviors of dynamic actors is an important task in many robotics
applications such as self-driving. It is extremely challenging as actors have latent intentions …
applications such as self-driving. It is extremely challenging as actors have latent intentions …
Improving multi-agent motion prediction with heuristic goals and motion refinement
C Gómez-Huélamo, MV Conde… - Proceedings of the …, 2023 - openaccess.thecvf.com
Motion Prediction (MP) of multiple surrounding agents in physical environments, and
accurate trajectory forecasting, is a crucial task for Autonomous Driving Stacks (ADS) and …
accurate trajectory forecasting, is a crucial task for Autonomous Driving Stacks (ADS) and …
Wayformer: Motion forecasting via simple & efficient attention networks
Motion forecasting for autonomous driving is a challenging task because complex driving
scenarios involve a heterogeneous mix of static and dynamic inputs. It is an open problem …
scenarios involve a heterogeneous mix of static and dynamic inputs. It is an open problem …
Learning lane graph representations for motion forecasting
We propose a motion forecasting model that exploits a novel structured map representation
as well as actor-map interactions. Instead of encoding vectorized maps as raster images, we …
as well as actor-map interactions. Instead of encoding vectorized maps as raster images, we …
Path-aware graph attention for hd maps in motion prediction
F Da, Y Zhang - 2022 International Conference on Robotics …, 2022 - ieeexplore.ieee.org
The success of motion prediction for autonomous driving relies on integration of information
from the HD maps. As maps are naturally graph-structured, investigation on graph neural …
from the HD maps. As maps are naturally graph-structured, investigation on graph neural …
Densetnt: End-to-end trajectory prediction from dense goal sets
Due to the stochasticity of human behaviors, predicting the future trajectories of road agents
is challenging for autonomous driving. Recently, goal-based multi-trajectory prediction …
is challenging for autonomous driving. Recently, goal-based multi-trajectory prediction …
Learning to predict vehicle trajectories with model-based planning
Predicting the future trajectories of on-road vehicles is critical for autonomous driving. In this
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …
Tpnet: Trajectory proposal network for motion prediction
Making accurate motion prediction of the surrounding traffic agents such as pedestrians,
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …