SOTAVerified

Motion Forecasting

Motion forecasting is the task of predicting the location of a tracked object in the future

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 205 papers

TitleStatusHype
Streaming Motion Forecasting for Autonomous DrivingCode1
Scene-aware Human Motion Forecasting via Mutual Distance PredictionCode1
MotionLM: Multi-Agent Motion Forecasting as Language ModelingCode1
SEPT: Towards Efficient Scene Representation Learning for Motion Prediction—0
Staged Contact-Aware Global Human Motion ForecastingCode0
Efficient Baselines for Motion Prediction in Autonomous DrivingCode1
Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked AutoencodersCode2
Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps—0
trajdata: A Unified Interface to Multiple Human Trajectory DatasetsCode2
Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction—0
The 2nd Place Solution for 2023 Waymo Open Sim Agents Challenge—0
SIMMF: Semantics-aware Interactive Multiagent Motion Forecasting for Autonomous Vehicle Driving—0
QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory PredictionCode2
Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?Code1
Stochastic Multi-Person 3D Motion ForecastingCode1
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud SequencesCode1
MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion—0
Regularizing Towards Soft Equivariance Under Mixed Symmetries—0
Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction—0
TSGN: Temporal Scene Graph Neural Networks with Projected Vectorized Representation for Multi-Agent Motion Prediction—0
Best Practices for 2-Body Pose ForecastingCode1
WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting—0
ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals—0
Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networks—0
Generative Causal Representation Learning for Out-of-Distribution Motion ForecastingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1WSTbrier-minFDE (K=6)26.41—Unverified
2BaseballBrobrier-minFDE (K=6)24.35—Unverified
3Constant velocity map priorbrier-minFDE (K=6)23.39—Unverified
4DQSbrier-minFDE (K=6)12.88—Unverified
5xjtub09brier-minFDE (K=6)11.46—Unverified
6suubrier-minFDE (K=6)9.51—Unverified
7yellowtaxibrier-minFDE (K=6)8.86—Unverified
8Online_CLbrier-minFDE (K=6)8.75—Unverified
9RuShengbrier-minFDE (K=6)8.38—Unverified
10mmmdddbrier-minFDE (K=6)8.26—Unverified