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#trajectory-prediction

3 curated events
papersTODAY 04:00 UTC

GEAR: From Dynamic Encoding to Dynamic Activation in Social Trajectory Prediction

A new arXiv paper introduces GEAR, a method for predicting human trajectories that must account for both how individuals move and how agents influence one another. The authors note that prior work has relied on attention mechanisms, graph structures, and temporal encoders to model these dynamics, and propose shifting the emphasis from dynamic encoding to dynamic activation. The work appears as a cross-listing on arXiv's cs.AI category.

arxiv/trajectory-prediction/attention/research
papersSEP 10 04:00 UTC

TempTPI: Informer-Based Trajectory Prediction for Maritime Vessels

A new arXiv paper introduces TempTPI, a model that adapts the Informer architecture to forecast the future paths of ships over long time horizons. The approach learns from AIS tracking data, where conventional Transformers can struggle with long input sequences. The authors target improvements in navigational safety and logistics planning for the maritime sector.

maritime/trajectory-prediction/transformers/safety
papersSEP 10 04:00 UTC

Researchers propose MAVEN-T for real-time multi-agent trajectory prediction in autonomous driving

A newly updated arXiv paper introduces MAVEN-T, a method combining reinforcement learning with heterogeneous knowledge distillation to forecast the future paths of multiple agents simultaneously. The work targets real-time deployment in autonomous vehicles, where anticipating surrounding traffic informs collision checking, planning, and control. The approach is designed to remain dependable in dense scenarios with diverse and multimodal agent behaviors.

autonomous-driving/trajectory-prediction/reinforcement-learning/real-time