Motionlm: Multi-agent motion forecasting as language modeling
Reliable forecasting of the future behavior of road agents is a critical component to safe
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …
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 …
Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction
B Varadarajan, A Hefny, A Srivastava… - … on Robotics and …, 2022 - ieeexplore.ieee.org
Predicting the future behavior of road users is one of the most challenging and important
problems in autonomous driving. Applying deep learning to this problem requires fusing …
problems in autonomous driving. Applying deep learning to this problem requires fusing …
Transformer networks for trajectory forecasting
Most recent successes on forecasting the people motion are based on LSTM models and all
most recent progress has been achieved by modelling the social interaction among people …
most recent progress has been achieved by modelling the social interaction among people …
Human trajectory forecasting in crowds: A deep learning perspective
Since the past few decades, human trajectory forecasting has been a field of active research
owing to its numerous real-world applications: evacuation situation analysis, deployment of …
owing to its numerous real-world applications: evacuation situation analysis, deployment of …
Multimodal trajectory prediction conditioned on lane-graph traversals
Accurately predicting the future motion of surrounding vehicles requires reasoning about the
inherent uncertainty in driving behavior. This uncertainty can be loosely decoupled into …
inherent uncertainty in driving behavior. This uncertainty can be loosely decoupled into …
M2i: From factored marginal trajectory prediction to interactive prediction
Predicting future motions of road participants is an important task for driving autonomously in
urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet …
urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet …
Hdgt: Heterogeneous driving graph transformer for multi-agent trajectory prediction via scene encoding
Encoding a driving scene into vector representations has been an essential task for
autonomous driving that can benefit downstream tasks eg, trajectory prediction. The driving …
autonomous driving that can benefit downstream tasks eg, trajectory prediction. The driving …
Stepwise goal-driven networks for trajectory prediction
We propose to predict the future trajectories of observed agents (eg, pedestrians or vehicles)
by estimating and using their goals at multiple time scales. We argue that the goal of a …
by estimating and using their goals at multiple time scales. We argue that the goal of a …
Evolvegraph: Multi-agent trajectory prediction with dynamic relational reasoning
Multi-agent interacting systems are prevalent in the world, from purely physical systems to
complicated social dynamic systems. In many applications, effective understanding of the …
complicated social dynamic systems. In many applications, effective understanding of the …