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Rui-Min Ke

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Preprint Sep 2026

Fewer Qubits, Better Choices: Coupling-Aware Sub-QUBO Selection for Quantum-Assisted Traffic Zone Partitioning

Near-term quantum optimizers accommodate roughly a hundred binary variables, requiring large Quadratic Unconstrained Binary Optimization problems to be decomposed into hardware-sized subproblems while other variables remain fixed. Variables are typically selected by ranking the objective changes from individual flips....

Qian-Wen Guo, Rui-Min Ke · 0 citations
Preprint Sep 2026

SimTIO: A Simulation-Grounded Multi-Agent LLM Framework for Compositional Traffic Intervention Optimization

SimTIO, a simulation-grounded multi-agent large language model framework for composing and selecting traffic interventions under explicit operational constraints, supports using LLMs as constrained, feedback-guided local search operators while reserving final decision authority for executable tools, microscopic simulat...

Shu-Yang Li, Rui-Min Ke · 0 citations
Preprint Aug 2026

Risk-Adaptive Edge--Cloud Visual Reasoning for Communication-Efficient Autonomous Driving

A risk-adaptive edge-cloud architecture in which onboard traffic assessment determines when cloud reasoning is requested is presented, in which onboard traffic assessment served as a practical trigger for selective VLM inference in these experiments.

Meng Ma, Shu-Yang Li, Nai-Gang Wang et al. · 0 citations
Jul 2026

CARLA-GS: Decoupling Representation, Reasoning, and Physics Simulation for Autonomous Driving Corner-Case Synthesis

This work proposes CARLA-GS, a modular corner-case synthesis pipeline that decouples visual representation, semantic reasoning, and physics-based execution while maintaining tight cross-module coupling, and experiments show that this framework enables controllable corner-case generation and produces photorealistic, spa...

Kaicong Huang, Meng Ma, Ruimin Ke · 1 citation
Preprint Aug 2026

Cooperative Platoon Routing and Dispatching via Edge-Assisted Hybrid Quantum Optimization

An edge-assisted, closed-loop evaluation pipeline for platooning-aware vehicle routing is developed and results suggest that edge perception and shallow quantum optimization can work together as a useful component of closed-loop CAV platoon dispatching.

T. Azfar, Ruimin Ke · 0 citations
#artificial intelligence Preprint Jul 2026

GHR-VLM: Making Zero-Shot Transit Video Analytics Realizable with Grounded Hybrid Reasoning

GHR-VLM, a visual grounded hybrid reasoning framework for zero-shot transit-bus video analytics, is proposed, motivated by the observation that explicit visual grounding can improve VLM reasoning by converting long surveillance streams into compact, passenger-centered spatiotemporal evidence.

Kaicong Huang, Weiheng Oh, Jack M. Reilly et al. · 0 citations

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