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 …
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 …
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 …
Rain: Reinforced hybrid attention inference network for motion forecasting
Motion forecasting plays a significant role in various domains (eg, autonomous driving,
human-robot interaction), which aims to predict future motion sequences given a set of …
human-robot interaction), which aims to predict future motion sequences given a set of …
Implicit latent variable model for scene-consistent motion forecasting
In order to plan a safe maneuver an autonomous vehicle must accurately perceive its
environment, and understand the interactions among traffic participants. In this paper, we …
environment, and understand the interactions among traffic participants. In this paper, we …
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 …
Query-centric trajectory prediction
Predicting the future trajectories of surrounding agents is essential for autonomous vehicles
to operate safely. This paper presents QCNet, a modeling framework toward pushing the …
to operate safely. This paper presents QCNet, a modeling framework toward pushing the …
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 …
Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks
V Kosaraju, A Sadeghian… - Advances in neural …, 2019 - proceedings.neurips.cc
Predicting the future trajectories of multiple interacting pedestrians in a scene has become
an increasingly important problem for many different applications ranging from control of …
an increasingly important problem for many different applications ranging from control of …
Tpcn: Temporal point cloud networks for motion forecasting
Abstract We propose the Temporal Point Cloud Networks (TPCN), a novel and flexible
framework with joint spatial and temporal learning for trajectory prediction. Unlike existing …
framework with joint spatial and temporal learning for trajectory prediction. Unlike existing …