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 …
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 …
Rethinking integration of prediction and planning in deep learning-based automated driving systems: a review
Automated driving has the potential to revolutionize personal, public, and freight mobility.
Besides the enormous challenge of perception, ie accurately perceiving the environment …
Besides the enormous challenge of perception, ie accurately perceiving the environment …
Adapt: Efficient multi-agent trajectory prediction with adaptation
Forecasting future trajectories of agents in complex traffic scenes requires reliable and
efficient predictions for all agents in the scene. However, existing methods for trajectory …
efficient predictions for all agents in the scene. However, existing methods for trajectory …
Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders
This study explores the application of self-supervised learning (SSL) to the task of motion
forecasting, an area that has not yet been extensively investigated despite the widespread …
forecasting, an area that has not yet been extensively investigated despite the widespread …
Real-time motion prediction via heterogeneous polyline transformer with relative pose encoding
The real-world deployment of an autonomous driving system requires its components to run
on-board and in real-time, including the motion prediction module that predicts the future …
on-board and in real-time, including the motion prediction module that predicts the future …
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 …
TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving
Vision-centric joint perception and prediction (PnP) has become an emerging trend in
autonomous driving research. It predicts the future states of the traffic participants in the …
autonomous driving research. It predicts the future states of the traffic participants in the …
Learn tarot with mentor: A meta-learned self-supervised approach for trajectory prediction
M Pourkeshavarz, C Chen… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Predicting diverse yet admissible trajectories that adhere to the map constraints is
challenging. Graph-based scene encoders have been proven effective for preserving local …
challenging. Graph-based scene encoders have been proven effective for preserving local …
Traj-mae: Masked autoencoders for trajectory prediction
Trajectory prediction has been a crucial task in building a reliable autonomous driving
system by anticipating possible dangers. One key issue is to generate consistent trajectory …
system by anticipating possible dangers. One key issue is to generate consistent trajectory …