Ltp: Lane-based trajectory prediction for autonomous driving
The reasonable trajectory prediction of surrounding traffic participants is crucial for
autonomous driving. Especially, how to predict multiple plausible trajectories is still a …
autonomous driving. Especially, how to predict multiple plausible trajectories is still a …
Laformer: Trajectory prediction for autonomous driving with lane-aware scene constraints
Existing trajectory prediction methods for autonomous driving typically rely on one-stage
trajectory prediction models which condition future trajectories on observed trajectories …
trajectory prediction models which condition future trajectories on observed trajectories …
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 …
Collaborative motion prediction via neural motion message passing
Motion prediction is essential and challenging for autonomous vehicles and social robots.
One challenge of motion prediction is to model the interaction among traffic actors, which …
One challenge of motion prediction is to model the interaction among traffic actors, which …
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 …
Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation
Predicting the trajectories of surrounding agents is an essential ability for autonomous
vehicles navigating through complex traffic scenes. The future trajectories of agents can be …
vehicles navigating through complex traffic scenes. The future trajectories of agents can be …
Lookout: Diverse multi-future prediction and planning for self-driving
In this paper, we present LookOut, a novel autonomy system that perceives the environment,
predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of …
predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of …
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 …
Multi-head attention for multi-modal joint vehicle motion forecasting
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 …
attention. It produces joint forecasts for all vehicles on a road scene as sequences of multi …
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 …