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University of Kansas

Academic institutionnorthamerica · us
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Research library123linked papers
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Selected work

Representative Papers

A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge

Oct 18, 2023arXiv.org

Managing and retrieving high-dimensional vector data poses significant challenges, particularly as traditional databases fail to meet performance requirements and the need for tight integration with large language models (LLMs) intensifies. Method: This paper systematically surveys four major approximate nearest neighbor search (ANNS) paradigms—hashing, tree-based indexing, graph-based methods (e.g., HNSW), and quantization (PQ/SQ)—and integrates hybrid optimization strategies. Contribution/Results: It introduces, for the first time, a “Four-Dimensional Methodology” framework tailored for industrial deployment of vector databases, analyzing trade-offs among accuracy, latency, memory footprint, and scalability. The work constructs a structured knowledge graph covering 200+ ANNS algorithms and proposes a novel paradigm for deep synergy between vector databases and LLMs. Collectively, these contributions provide both theoretical foundations and practical guidelines for system selection, architectural design, and development of AI-native database systems.

62 citations3 influentialRead paper

Motion Planning for Autonomous Vehicles: When Model Predictive Control Meets Ensemble Kalman Smoothing

Jul 10, 2024American Control Conference

Nonlinear model predictive control (NMPC) for motion planning using neural-network-based vehicle models suffers from high computational cost and poor real-time performance due to the inherent nonconvexity of the underlying optimization problem. Method: This paper reformulates NMPC as a Bayesian estimation problem—its first such formulation—thereby circumventing traditional numerical optimization bottlenecks. We propose an efficient solution framework based on the ensemble Kalman smoother (EnKS), integrating nonlinear dynamical modeling with sequential data assimilation principles. The approach requires neither gradient evaluation nor iterative optimization, drastically reducing computational complexity. Contribution/Results: Simulation results demonstrate a 100×–1000× improvement in planning speed while preserving trajectory accuracy and closed-loop stability. This work establishes a novel paradigm for real-time NMPC leveraging learned vehicle models, enabling practical deployment in safety-critical autonomous driving applications.

1 citationsRead paper
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