MSHL learns higher-order group relations from incomplete spatiotemporal observations via adaptive multi-scale hypergraph Laplacians and a safe neural refinement stage that improves imputation when structure is present.
Ai2-active safety: Ai-enabled interaction-aware active safety analysis with vehicle dynamics
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Evasive acceleration quantifies driving risk as the minimum 2D constant relative acceleration needed to avoid collision and outperforms time-to-collision on warning timing, discrimination, and information retention across crash datasets.
OKH-RAG represents knowledge as ordered hyperedges and retrieves coherent interaction sequences via a learned transition model, outperforming permutation-invariant RAG baselines on order-sensitive QA tasks.
citing papers explorer
-
Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement
MSHL learns higher-order group relations from incomplete spatiotemporal observations via adaptive multi-scale hypergraph Laplacians and a safe neural refinement stage that improves imputation when structure is present.
-
Driving risk emerges from the required two-dimensional joint evasive acceleration
Evasive acceleration quantifies driving risk as the minimum 2D constant relative acceleration needed to avoid collision and outperforms time-to-collision on warning timing, discrimination, and information retention across crash datasets.
-
Knowledge Is Not Static: Order-Aware Hypergraph RAG for Language Models
OKH-RAG represents knowledge as ordered hyperedges and retrieves coherent interaction sequences via a learned transition model, outperforming permutation-invariant RAG baselines on order-sensitive QA tasks.