#trajectory-prediction News & Analysis

4 articles tagged with #trajectory-prediction. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · Mar 96/10

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XR-DT: Extended Reality-Enhanced Digital Twin for Safe Motion Planning via Human-Aware Model Predictive Path Integral Control

Researchers developed XR-DT, an Extended Reality-enhanced Digital Twin framework that combines augmented, virtual, and mixed reality to improve human-robot interaction in shared workspaces. The system uses a novel Human-Aware Model Predictive Path Integral control model with ATLAS, a Transformer-based trajectory prediction system, to enable safer and more interpretable robot navigation around humans.

AINeutralarXiv – CS AI · Mar 45/103

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ShipTraj-R1: Reinforcing Ship Trajectory Prediction in Large Language Models via Group Relative Policy Optimization

Researchers propose ShipTraj-R1, a novel LLM-based framework using group relative policy optimization (GRPO) for ship trajectory prediction. The system reformulates trajectory prediction as a text-to-text generation problem and demonstrates superior performance compared to existing deep learning baselines on real-world maritime datasets.

AINeutralarXiv – CS AI · Mar 175/10

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TrajMamba: An Ego-Motion-Guided Mamba Model for Pedestrian Trajectory Prediction from an Egocentric Perspective

Researchers propose TrajMamba, a new AI model that uses Mamba architecture to predict pedestrian movement from an ego-centric perspective for autonomous driving applications. The model integrates pedestrian motion and ego-vehicle movement data to achieve state-of-the-art performance on PIE and JAAD datasets.

AINeutralarXiv – CS AI · Mar 54/10

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TPK: Trustworthy Trajectory Prediction Integrating Prior Knowledge For Interpretability and Kinematic Feasibility

Researchers developed TPK, a trajectory prediction system for autonomous vehicles that integrates prior knowledge to make predictions more trustworthy and physically feasible. The system incorporates interaction and kinematic models for vehicles, pedestrians, and cyclists, improving interpretability while ensuring predictions adhere to physics.