Jfp: Joint future prediction with interactive multi-agent modeling for autonomous driving
Abstract We propose\textit {JFP}, a Joint Future Prediction model that can learn to generate
accurate and consistent multi-agent future trajectories. For this task, many different methods …
accurate and consistent multi-agent future trajectories. For this task, many different methods …
BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction
Predicting future trajectories of surrounding agents is essential for safety-critical autonomous
driving. Most existing work focuses on predicting marginal trajectories for each agent …
driving. Most existing work focuses on predicting marginal trajectories for each agent …
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 …
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 …
Multiple futures prediction
C Tang, RR Salakhutdinov - Advances in neural information …, 2019 - proceedings.neurips.cc
Temporal prediction is critical for making intelligent and robust decisions in complex
dynamic environments. Motion prediction needs to model the inherently uncertain future …
dynamic environments. Motion prediction needs to model the inherently uncertain future …
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 …
Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset
As autonomous driving systems mature, motion forecasting has received increasing
attention as a critical requirement for planning. Of particular importance are interactive …
attention as a critical requirement for planning. Of particular importance are interactive …
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 …
INT2: Interactive Trajectory Prediction at Intersections
Motion forecasting is an important component in autonomous driving systems. One of the
most challenging problems in motion forecasting is interactive trajectory prediction, whose …
most challenging problems in motion forecasting is interactive trajectory prediction, whose …
Pip: Planning-informed trajectory prediction for autonomous driving
It is critical to predict the motion of surrounding vehicles for self-driving planning, especially
in a socially compliant and flexible way. However, future prediction is challenging due to the …
in a socially compliant and flexible way. However, future prediction is challenging due to the …