Framework learns latent scene embeddings from 24 trajectory datasets to produce transferability scores that correlate with cross-dataset model performance.
Grip++: Enhanced graph-based interaction-aware trajectory prediction for autonomous driving
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
A new online attack framework manipulates object poses in shared CAV perception data below detection thresholds, propagating errors to cause unsafe trajectory predictions and behaviors in up to 50% of tested scenarios while evading defenses.
EdgeVTP delivers the lowest measured end-to-end latency on Jetson-class platforms while matching or exceeding state-of-the-art accuracy on highway trajectory benchmarks by using bounded graph interactions and a one-shot curve decoder.
Introduces RHP module using continuous learnable potential field for dynamic risk profiling in trajectory prediction, reporting 25% RMSE and 29.1% minFDE reductions on highD and SHRP2 datasets.
Empirical comparison of LSTM, GNN, and Transformer architectures for NBA trajectory forecasting finds hybrid LSTM with contextual information yields lowest FDE of 1.51m over horizons up to 2s.
citing papers explorer
-
Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings
Framework learns latent scene embeddings from 24 trajectory datasets to produce transferability scores that correlate with cross-dataset model performance.
-
From Stealthy Data Fabrication to Unsafe Driving: Realistic Scenario Attacks on Collaborative Perception
A new online attack framework manipulates object poses in shared CAV perception data below detection thresholds, propagating errors to cause unsafe trajectory predictions and behaviors in up to 50% of tested scenarios while evading defenses.
-
EdgeVTP: Exploration of Latency-efficient Trajectory Prediction for Edge-based Embedded Vision Applications
EdgeVTP delivers the lowest measured end-to-end latency on Jetson-class platforms while matching or exceeding state-of-the-art accuracy on highway trajectory benchmarks by using bounded graph interactions and a one-shot curve decoder.
-
From Cues to Horizons: Dynamic Risk Horizon Profiling for Trajectory Prediction
Introduces RHP module using continuous learnable potential field for dynamic risk profiling in trajectory prediction, reporting 25% RMSE and 29.1% minFDE reductions on highD and SHRP2 datasets.
-
Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers
Empirical comparison of LSTM, GNN, and Transformer architectures for NBA trajectory forecasting finds hybrid LSTM with contextual information yields lowest FDE of 1.51m over horizons up to 2s.