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Hochschule München

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Research library33linked papers
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Selected work

Representative Papers

Domus: An Open-Data Web Platform for House History Research

Aug 12, 2026

This work addresses the lack of accessible tools for genealogists and local historians to contribute to and discover architectural historical data by proposing a fully client-side, open collaborative platform that requires no custom backend. The platform uniquely integrates Wikidata’s time-qualified property model with OpenHistoricalMap’s historical building footprints, enabling structured crowdsourcing through an interactive map interface. Leveraging public APIs from both services, it performs authentication and read/write operations entirely in the browser, storing all contributions under the CC0 license in a public database. The system supports visual editing and querying of genealogically relevant information—including building occupants, owners, and address changes—thereby significantly enhancing the accessibility of open historical data and fostering greater community engagement.

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Random Inverse Problems with Structural and Probabilistic Ambiguities

Aug 02, 2026

This work addresses the challenge of coexisting structural ambiguity—arising from non-injectivity of the forward model—and probabilistic ambiguity—stemming from parameter uncertainty—in stochastic inverse problems involving nonlinear parameter dependencies and observational uncertainties. To tackle this, the authors propose a unified modeling framework that couples both types of ambiguity for the first time by representing parameter uncertainty through a mixture of probability densities and formulating a Bayesian inversion-based posterior inference algorithm. The approach is validated on one- and two-dimensional quadratic forward models, demonstrating its ability to accurately resolve probabilistic ambiguity, visualize residual structural ambiguity, and handle both finite and infinite solution sets. Furthermore, it reveals the interaction mechanisms between the two forms of ambiguity within the posterior distribution.

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CommandLM: Data driven behavior level descriptor for ego vehicles

Jul 24, 2026

Current autonomous driving systems lack interpretable, behavior-level decision descriptions, which hinders their safety, trustworthiness, and regulatory compliance. This work proposes a multimodal large language model framework that, for the first time, integrates quantized LoRA-finetuned LLMs with Q-Former adapters to efficiently map spatiotemporal fusion of LiDAR and multi-camera bird’s-eye-view inputs into human-readable behavioral semantics. Evaluated on the newly curated CommandLM-nuScenes dataset, the approach substantially outperforms the BLIP-2 baseline, achieving CIDEr and BERT-F1 scores of 0.67 and 0.88, respectively. Human evaluation confirms that 58% of the generated descriptions are accurate, concise, and compliant with driving norms.

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Recent publications

Latest Papers

Domus: An Open-Data Web Platform for House History Research

Aug 12, 2026

This work addresses the lack of accessible tools for genealogists and local historians to contribute to and discover architectural historical data by proposing a fully client-side, open collaborative platform that requires no custom backend. The platform uniquely integrates Wikidata’s time-qualified property model with OpenHistoricalMap’s historical building footprints, enabling structured crowdsourcing through an interactive map interface. Leveraging public APIs from both services, it performs authentication and read/write operations entirely in the browser, storing all contributions under the CC0 license in a public database. The system supports visual editing and querying of genealogically relevant information—including building occupants, owners, and address changes—thereby significantly enhancing the accessibility of open historical data and fostering greater community engagement.

0 citationsRead paper

Random Inverse Problems with Structural and Probabilistic Ambiguities

Aug 02, 2026

This work addresses the challenge of coexisting structural ambiguity—arising from non-injectivity of the forward model—and probabilistic ambiguity—stemming from parameter uncertainty—in stochastic inverse problems involving nonlinear parameter dependencies and observational uncertainties. To tackle this, the authors propose a unified modeling framework that couples both types of ambiguity for the first time by representing parameter uncertainty through a mixture of probability densities and formulating a Bayesian inversion-based posterior inference algorithm. The approach is validated on one- and two-dimensional quadratic forward models, demonstrating its ability to accurately resolve probabilistic ambiguity, visualize residual structural ambiguity, and handle both finite and infinite solution sets. Furthermore, it reveals the interaction mechanisms between the two forms of ambiguity within the posterior distribution.

0 citationsRead paper

CommandLM: Data driven behavior level descriptor for ego vehicles

Jul 24, 2026

Current autonomous driving systems lack interpretable, behavior-level decision descriptions, which hinders their safety, trustworthiness, and regulatory compliance. This work proposes a multimodal large language model framework that, for the first time, integrates quantized LoRA-finetuned LLMs with Q-Former adapters to efficiently map spatiotemporal fusion of LiDAR and multi-camera bird’s-eye-view inputs into human-readable behavioral semantics. Evaluated on the newly curated CommandLM-nuScenes dataset, the approach substantially outperforms the BLIP-2 baseline, achieving CIDEr and BERT-F1 scores of 0.67 and 0.88, respectively. Human evaluation confirms that 58% of the generated descriptions are accurate, concise, and compliant with driving norms.

0 citationsRead paper