Trajectory Prediction
251 papers with code • 29 benchmarks • 34 datasets
Trajectory Prediction is the problem of predicting the short-term (1-3 seconds) and long-term (3-5 seconds) spatial coordinates of various road-agents such as cars, buses, pedestrians, rickshaws, and animals, etc. These road-agents have different dynamic behaviors that may correspond to aggressive or conservative driving styles.
Source: Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs
Benchmarks
These leaderboards are used to track progress in Trajectory Prediction
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Libraries
Use these libraries to find Trajectory Prediction models and implementationsDatasets
nuScenes
Argoverse
UCY
highD Dataset
INTERACTION Dataset
ApolloScape
Waymo Open Motion Dataset
ETH
inD Dataset
HDD
Latest papers
T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory
daeheepark/t4p
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Our method surpasses the performance of existing state-of-the-art online learning methods in terms of both prediction accuracy and computational efficiency.
A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving
In autonomous vehicle (AV) technology, the ability to accurately predict the movements of surrounding vehicles is paramount for ensuring safety and operational efficiency.
SocialCVAE: Predicting Pedestrian Trajectory via Interaction Conditioned Latents
Pedestrian trajectory prediction is the key technology in many applications for providing insights into human behavior and anticipating human future motions.
UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation
logan-lin/uvtm
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However, most methods target only one specific task and cannot be applied universally.
Robot Trajectron: Trajectory Prediction-based Shared Control for Robot Manipulation
We address the problem of (a) predicting the trajectory of an arm reaching motion, based on a few seconds of the motion's onset, and (b) leveraging this predictor to facilitate shared-control manipulation tasks, easing the cognitive load of the operator by assisting them in their anticipated direction of motion.
Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data
basiralab/fedgmte-net-plus
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The three key innovations of FedGmTE-Net++ are: (i) presenting the first federated learning framework specifically designed for brain multi-trajectory evolution prediction in a data-scarce environment, (ii) incorporating an auxiliary regularizer in the local objective function to exploit all the longitudinal brain connectivity within the evolution trajectory and maximize data utilization, (iii) introducing a two-step imputation process, comprising a preliminary KNN-based precompletion followed by an imputation refinement step that employs regressors to improve similarity scores and refine imputations.
STIGCN: spatial–temporal interaction‑aware graph convolution network for pedestrian trajectory prediction
Chenwangxing/STIGCN_master
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The Journal of Supercomputing 2023
STIGCN considers the correlation between social interaction and pedestrian movement factors to achieve more accurate interaction modeling.
Social-Transmotion: Promptable Human Trajectory Prediction
vita-epfl/social-transmotion
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We translate the idea of a prompt from Natural Language Processing (NLP) to the task of human trajectory prediction, where a prompt can be a sequence of x-y coordinates on the ground, bounding boxes in the image plane, or body pose keypoints in either 2D or 3D.
Improving Transferability for Cross-domain Trajectory Prediction via Neural Stochastic Differential Equation
To address this limitation, we propose a method based on continuous and stochastic representations of Neural Stochastic Differential Equations (NSDE) for alleviating discrepancies due to data acquisition strategy.
Multiple Hypothesis Dropout: Estimating the Parameters of Multi-Modal Output Distributions
dngu7/multiple-hypothesis-dropout
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In many real-world applications, from robotics to pedestrian trajectory prediction, there is a need to predict multiple real-valued outputs to represent several potential scenarios.
