motion prediction
185 papers with code • 0 benchmarks • 13 datasets
Benchmarks
These leaderboards are used to track progress in motion prediction
Libraries
Use these libraries to find motion prediction models and implementationsDatasets
UCY
Waymo Open Motion Dataset
Expi
Argoverse 2 Motion Forecasting
SinD
GTA-IM Dataset
NTU RGB+D 2D
Lyft Level 5 Prediction
Latest papers
OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment
The task of motion prediction is pivotal for autonomous driving systems, providing crucial data to choose a vehicle behavior strategy within its surroundings.
PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving
sair-lab/physord
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To bridge this gap, we present PhysORD, a neural-symbolic approach integrating the conservation law, i. e., the Euler-Lagrange equation, into data-driven neural models for motion prediction in off-road driving.
Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations
kwwcv/selfmotion
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To this end, we explore the feasibility of self-supervised motion prediction with only unlabeled LiDAR point clouds.
SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction
Context information, such as road maps and surrounding agents' states, provides crucial geometric and semantic information for motion behavior prediction.
Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting
Human motion prediction is still an open problem extremely important for autonomous driving and safety applications.
GenAD: Generative End-to-End Autonomous Driving
We then employ a variational autoencoder to learn the future trajectory distribution in a structural latent space for trajectory prior modeling.
SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving
hkust-aerial-robotics/simpl
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This paper presents a Simple and effIcient Motion Prediction baseLine (SIMPL) for autonomous vehicles.
Data-Driven Prediction of Seismic Intensity Distributions Featuring Hybrid Classification-Regression Models
Furthermore, the proposed model can predict even abnormal seismic intensity distributions, a task at conventional GMPEs often struggle.
Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals
bshfang/self-supervised-motion
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Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an emerging research for robotics and autonomous driving.
GCNext: Towards the Unity of Graph Convolutions for Human Motion Prediction
The past few years has witnessed the dominance of Graph Convolutional Networks (GCNs) over human motion prediction. Various styles of graph convolutions have been proposed, with each one meticulously designed and incorporated into a carefully-crafted network architecture.
