Trajectory Forecasting
73 papers with code • 4 benchmarks • 16 datasets
Trajectory forecasting is a sequential prediction task, where a forecasting model predicts future trajectories of all moving agents (humans, vehicles, etc.) in a scene, based on their past trajectories and/or the scene context.
(Illustrative figure from Social NCE: Contrastive Learning of Socially-aware Motion Representations)
Benchmarks
These leaderboards are used to track progress in Trajectory Forecasting
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Datasets
UCY
highD Dataset
inD Dataset
JAAD
TrajNet
rounD Dataset
FPL
PIE
exiD Dataset
TrajAir: A General Aviation Trajectory Dataset
Latest papers with no code
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.
Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving
Although these models have conventionally been evaluated for open-loop prediction, we show that they can be used to parameterize autoregressive closed-loop models without retraining.
TrackGPT -- A generative pre-trained transformer for cross-domain entity trajectory forecasting
The forecasting of entity trajectories at future points in time is a critical capability gap in applications across both Commercial and Defense sectors.
Uncovering the human motion pattern: Pattern Memory-based Diffusion Model for Trajectory Prediction
To uncover latent motion patterns in human behavior, we introduce a novel memory-based method, named Motion Pattern Priors Memory Network.
Cooperative Probabilistic Trajectory Forecasting under Occlusion
Occlusion-aware planning often requires communicating the information of the occluded object to the ego agent for safe navigation.
Probabilistic Feature Augmentation for AIS-Based Multi-Path Long-Term Vessel Trajectory Forecasting
This study explores using AIS data to prevent vessel-to-whale collisions by forecasting long-term vessel trajectories from engineered AIS data sequences.
Inferring Relational Potentials in Interacting Systems
In this work, we propose Neural Interaction Inference with Potentials (NIIP) as an alternative approach to discover such interactions that enables greater flexibility in trajectory modeling: it discovers a set of relational potentials, represented as energy functions, which when minimized reconstruct the original trajectory.
KI-PMF: Knowledge Integrated Plausible Motion Forecasting
Accurately forecasting the motion of traffic actors is crucial for the deployment of autonomous vehicles at a large scale.
A Diffusion-Model of Joint Interactive Navigation
Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors.
Pre-training on Synthetic Driving Data for Trajectory Prediction
We propose to augment both HD maps and trajectories and apply pre-training strategies on top of them.
