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#trajectory-prediction

TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction

arXiv cs.AI · 2026-06-30 Cached

This paper introduces TrajRS, an extension of Randomized Smoothing that provides certified robust radii for pedestrian trajectory predictors, offering verifiable safety guarantees against adversarial perturbations.

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Learn to Quantify Social Interaction with Constraints for Pedestrian Walking

arXiv cs.AI · 2026-06-17 Cached

This paper introduces a method called 'Learn to Cluster' to quantify and interpret social interactions among pedestrians for better trajectory prediction. It uses probabilistic latent variable generative learning to cluster social interactions without labels, improving robustness for autonomous driving and social robots.

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M-CTX: Exact and Scalable Spatial Context Retrieval for Trajectory Analytics

arXiv cs.LG · 2026-06-16 Cached

This paper introduces M-CTX, an exact and scalable spatial context retrieval framework for trajectory analytics that reduces context construction time from 17 CPU-days to 1.8 hours on a 5.48M-anchor maritime corpus, by replacing brute-force stages with index-backed operators.

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A Comparative Study of Graph Neural Network Layer Selection for Interaction Modelling in Driving Trajectory Prediction

arXiv cs.LG · 2026-06-16 Cached

This paper compares 19 graph neural network layer types for modelling interactions in driving trajectory prediction, finding ARMA, Chebyshev, and topology-aware layers most effective and offering design principles for better prediction models.

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Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments

Hugging Face Daily Papers · 2026-05-28 Cached

Qwen-VLA is a unified vision-language-action model for embodied decision-making, integrating manipulation, navigation, and trajectory prediction across different robot platforms. It uses a DiT-based action decoder and embodiment-aware prompt conditioning, achieving strong performance and out-of-distribution generalization.

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SleepWalk: A Three-Tier Benchmark for Stress-Testing Instruction-Guided Vision-Language Navigation

Hugging Face Daily Papers · 2026-05-11 Cached

SleepWalk is a three-tier benchmark for evaluating vision-language models' ability to predict spatially coherent trajectories in 3D environments from textual instructions and visual observations, revealing systematic failures in grounded spatial reasoning under occlusions and multi-step instructions.

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OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation

Hugging Face Daily Papers · 2026-04-20 Cached

OneVL is a unified vision-language-action framework that compresses chain-of-thought reasoning into latent tokens supervised by both language and visual world model decoders, achieving state-of-the-art trajectory prediction accuracy for autonomous driving at answer-only inference latency. It is the first latent CoT method to surpass explicit CoT across four benchmarks.

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