Motion Forecasting
66 papers with code • 1 benchmarks • 12 datasets
Motion forecasting is the task of predicting the location of a tracked object in the future
Benchmarks
These leaderboards are used to track progress in Motion Forecasting
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Datasets
Argoverse
Waymo Open Motion Dataset
Argoverse-HD
Argoverse 2 Motion Forecasting
SinD
Argoverse 2 Sensor
Lyft Level 5 Prediction
Argoverse 2 Lidar
Latest papers with no code
Scaling Motion Forecasting Models with Ensemble Distillation
These experiments demonstrate distillation from ensembles as an effective method for improving accuracy of predictive models for robotic systems with limited compute budgets.
Hierarchical Light Transformer Ensembles for Multimodal Trajectory Forecasting
Accurate trajectory forecasting is crucial for the performance of various systems, such as advanced driver-assistance systems and self-driving vehicles.
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving
As an essential task in autonomous driving (AD), motion prediction aims to predict the future states of surround objects for navigation.
Large Language Models Powered Context-aware Motion Prediction
Traditional methods of motion forecasting primarily encode vector information of maps and historical trajectory data of traffic participants, lacking a comprehensive understanding of overall traffic semantics, which in turn affects the performance of prediction tasks.
GazeMotion: Gaze-guided Human Motion Forecasting
We present GazeMotion, a novel method for human motion forecasting that combines information on past human poses with human eye gaze.
Fooling Neural Networks for Motion Forecasting via Adversarial Attacks
Human motion prediction is still an open problem, which is extremely important for autonomous driving and safety applications.
Respiratory motion forecasting with online learning of recurrent neural networks for safety enhancement in externally guided radiotherapy
We use UORO, SnAp-1, and DNI to forecast each marker's 3D position with horizons (the time interval in advance for which the prediction is made) h<=2. 1s and compare them with RTRL, least mean squares, and linear regression.
Robot Interaction Behavior Generation based on Social Motion Forecasting for Human-Robot Interaction
Integrating robots into populated environments is a complex challenge that requires an understanding of human social dynamics.
FIMP: Future Interaction Modeling for Multi-Agent Motion Prediction
Multi-agent motion prediction is a crucial concern in autonomous driving, yet it remains a challenge owing to the ambiguous intentions of dynamic agents and their intricate interactions.
TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding
Humans commonly work with multiple objects in daily life and can intuitively transfer manipulation skills to novel objects by understanding object functional regularities.
