Selected work

Representative Papers

Assessing the Impact of Fleet Size on Crowdsourced Mapping Using a Dissimilarity Measure

Sep 24, 2026

This study addresses the unclear relationship between fleet size and mapping quality in crowdsourced mapping, as well as the high cost and limited scalability of traditional surveying methods. To this end, it proposes a multi-vehicle cooperative perception simulation framework based on the GOSPAM metric. By integrating localization errors with detection performance (false positives and false negatives), and incorporating spatial clustering, semantic filtering, and real-world trajectory simulation, the framework effectively quantifies individual fleet contributions. Experimental evaluations across fleet sizes ranging from 5 to 50 vehicles demonstrate that GOSPAM precisely assesses early vehicle contributions and accuracy gains in large-scale fleets. Consequently, this work provides a scalable, quantitative evaluation approach for maintaining crowdsourced maps.

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Geometric Attractor Monitoring: A Robust and Frugal Framework for Multi-modal Industrial Robotic Cycles

Aug 31, 2026

本文针对工业机器人多模态运行周期监控问题，提出基于相空间重构的几何吸引子监测框架，通过离散支持估计提供有效的健康指标。

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Why Model Credibility Isn't Enough: -Rethinking Trust in Simulation Architectures

Jun 16, 2026

This study addresses a critical limitation in existing simulation credibility assessment approaches, which predominantly focus on individual models and thus fail to capture the reliability of complex, multi-model architectures. Moving beyond the single-model evaluation paradigm, this work redefines trustworthiness at the architectural level and proposes a multidimensional framework that integrates sensitivity analysis, expert knowledge, explainable artificial intelligence, and complex network modeling. Through a systematic comparison of diverse methodologies across dimensions such as methodological rigor, generalizability, and computational resource demands, the research offers both theoretical foundations and practical guidance for constructing high-assurance simulation architectures.

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Recent publications

Latest Papers

Assessing the Impact of Fleet Size on Crowdsourced Mapping Using a Dissimilarity Measure

Sep 24, 2026

This study addresses the unclear relationship between fleet size and mapping quality in crowdsourced mapping, as well as the high cost and limited scalability of traditional surveying methods. To this end, it proposes a multi-vehicle cooperative perception simulation framework based on the GOSPAM metric. By integrating localization errors with detection performance (false positives and false negatives), and incorporating spatial clustering, semantic filtering, and real-world trajectory simulation, the framework effectively quantifies individual fleet contributions. Experimental evaluations across fleet sizes ranging from 5 to 50 vehicles demonstrate that GOSPAM precisely assesses early vehicle contributions and accuracy gains in large-scale fleets. Consequently, this work provides a scalable, quantitative evaluation approach for maintaining crowdsourced maps.

0 citationsRead paper

Geometric Attractor Monitoring: A Robust and Frugal Framework for Multi-modal Industrial Robotic Cycles

Aug 31, 2026

本文针对工业机器人多模态运行周期监控问题，提出基于相空间重构的几何吸引子监测框架，通过离散支持估计提供有效的健康指标。

0 citationsRead paper

Why Model Credibility Isn't Enough: -Rethinking Trust in Simulation Architectures

Jun 16, 2026

This study addresses a critical limitation in existing simulation credibility assessment approaches, which predominantly focus on individual models and thus fail to capture the reliability of complex, multi-model architectures. Moving beyond the single-model evaluation paradigm, this work redefines trustworthiness at the architectural level and proposes a multidimensional framework that integrates sensitivity analysis, expert knowledge, explainable artificial intelligence, and complex network modeling. Through a systematic comparison of diverse methodologies across dimensions such as methodological rigor, generalizability, and computational resource demands, the research offers both theoretical foundations and practical guidance for constructing high-assurance simulation architectures.

0 citationsRead paper