Latent variable sequential set transformers for joint multi-agent motion prediction
Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A
major challenge is to efficiently learn a representation that approximates the true joint …
major challenge is to efficiently learn a representation that approximates the true joint …
Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting
Predicting accurate future trajectories of multiple agents is essential for autonomous systems
but is challenging due to the complex interaction between agents and the uncertainty in …
but is challenging due to the complex interaction between agents and the uncertainty in …
Scene transformer: A unified architecture for predicting multiple agent trajectories
Predicting the motion of multiple agents is necessary for planning in dynamic environments.
This task is challenging for autonomous driving since agents (eg vehicles and pedestrians) …
This task is challenging for autonomous driving since agents (eg vehicles and pedestrians) …
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 …
Ipcc-tp: Utilizing incremental pearson correlation coefficient for joint multi-agent trajectory prediction
Reliable multi-agent trajectory prediction is crucial for the safe planning and control of
autonomous systems. Compared with single-agent cases, the major challenge in …
autonomous systems. Compared with single-agent cases, the major challenge in …
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 …
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 …
Hivt: Hierarchical vector transformer for multi-agent motion prediction
Accurately predicting the future motions of surrounding traffic agents is critical for the safety
of autonomous vehicles. Recently, vectorized approaches have dominated the motion …
of autonomous vehicles. Recently, vectorized approaches have dominated the motion …
Fjmp: Factorized joint multi-agent motion prediction over learned directed acyclic interaction graphs
Predicting the future motion of road agents is a critical task in an autonomous driving
pipeline. In this work, we address the problem of generating a set of scene-level, or joint …
pipeline. In this work, we address the problem of generating a set of scene-level, or joint …
Social-ssl: Self-supervised cross-sequence representation learning based on transformers for multi-agent trajectory prediction
Earlier trajectory prediction approaches focus on ways of capturing sequential structures
among pedestrians by using recurrent networks, which is known to have some limitations in …
among pedestrians by using recurrent networks, which is known to have some limitations in …