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 …
Behavioral intention prediction in driving scenes: A survey
In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …
Trajectory unified transformer for pedestrian trajectory prediction
Pedestrian trajectory prediction is an essentially connecting link to understanding human
behavior. Recent works achieve state-of-the-art performance gained from the hand …
behavior. Recent works achieve state-of-the-art performance gained from the hand …
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 …
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 …
Hpnet: Dynamic trajectory forecasting with historical prediction attention
Predicting the trajectories of road agents is essential for autonomous driving systems. The
recent mainstream methods follow a static paradigm which predicts the future trajectory by …
recent mainstream methods follow a static paradigm which predicts the future trajectory by …
Macformer: Map-agent coupled transformer for real-time and robust trajectory prediction
C Feng, H Zhou, H Lin, Z Zhang, Z Xu… - IEEE Robotics and …, 2023 - ieeexplore.ieee.org
Predicting the future behavior of agents is a fundamental task in autonomous vehicle
domains. Accurate prediction relies on comprehending the surrounding map, which …
domains. Accurate prediction relies on comprehending the surrounding map, which …
Improving multi-agent motion prediction with heuristic goals and motion refinement
C Gómez-Huélamo, MV Conde… - Proceedings of the …, 2023 - openaccess.thecvf.com
Motion Prediction (MP) of multiple surrounding agents in physical environments, and
accurate trajectory forecasting, is a crucial task for Autonomous Driving Stacks (ADS) and …
accurate trajectory forecasting, is a crucial task for Autonomous Driving Stacks (ADS) and …
A multi-task learning network with a collision-aware graph transformer for traffic-agents trajectory prediction
It is critical for autonomous vehicles to accurately forecast the future trajectories of
surrounding agents to avoid collisions. However, capturing the complex interactions …
surrounding agents to avoid collisions. However, capturing the complex interactions …
Deep Learning-based Motion Prediction Leveraging Autonomous Driving Datasets: State-of-the-Art
Autonomous vehicles continue to advance, primarily due to the continuous development
and improvement of deep learning methods. Motion prediction of road users is a function …
and improvement of deep learning methods. Motion prediction of road users is a function …