Pedestrian Trajectory Prediction
37 papers with code • 1 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Pedestrian Trajectory Prediction
| Trend | Dataset | Best Model | Paper | Code | Compare |
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JAAD
PIE
Euro-PVI
Latest papers with no code
LG-Traj: LLM Guided Pedestrian Trajectory Prediction
We introduce LG-Traj, a novel approach incorporating LLMs to generate motion cues present in pedestrian past/observed trajectories.
Recurrent Aligned Network for Generalized Pedestrian Trajectory Prediction
Previous studies have tried to tackle this problem by leveraging a portion of the trajectory data from the target domain to adapt the model.
GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction
Instead of predicting the future trajectory directly, we propose to use scene context and observed trajectory to predict the goal points first, and then reuse the goal points to predict the future trajectories.
AMEND: A Mixture of Experts Framework for Long-tailed Trajectory Prediction
Such a long-tail effect causes prediction models to underperform on the tail portion of the data distribution containing safety-critical scenarios.
Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction
To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets.
GSGFormer: Generative Social Graph Transformer for Multimodal Pedestrian Trajectory Prediction
Pedestrian trajectory prediction, vital for selfdriving cars and socially-aware robots, is complicated due to intricate interactions between pedestrians, their environment, and other Vulnerable Road Users.
S-T CRF: Spatial-Temporal Conditional Random Field for Human Trajectory Prediction
Trajectory prediction is of significant importance in computer vision.
Sparse Pedestrian Character Learning for Trajectory Prediction
Specifically, TSNet learns the negative-removed characters in the sparse character representation stream to improve the trajectory embedding obtained in the trajectory representation stream.
IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction
In the proposed module, the data-driven mechanism can effectively extract the feature representations of dynamic human-human interactions in the scene and calculate the corresponding weights to represent the importance of different interactions.
GBD-TS: Goal-based Pedestrian Trajectory Prediction with Diffusion using Tree Sampling Algorithm
GBD combines goal prediction with the diffusion network.
