LIPIcs.GIScience.2023.44.pdf
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Framework for Motorcycle Risk Assessment Using Onboard Panoramic Camera (Short Paper)
Authors
Natchapon Jongwiriyanurak 
,
Zichao Zeng ,
Meihui Wang ,
James Haworth ,
Garavig Tanaksaranond ,
Jan Boehm
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Part of:
Volume:
12th International Conference on Geographic Information Science (GIScience 2023)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Geographic Information Science (GIScience) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-09-07
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Document Identifiers
Author Details
- Department of Civil, Environmental and Geomatic Engineering, University College London, UK
- Department of Civil, Environmental and Geomatic Engineering, University College London, UK
- Department of Civil, Environmental and Geomatic Engineering, University College London, UK
- Department of Civil, Environmental and Geomatic Engineering, University College London, UK
- Department of Survey Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
Cite AsGet BibTex
Natchapon Jongwiriyanurak, Zichao Zeng, Meihui Wang, James Haworth, Garavig Tanaksaranond, and Jan Boehm. Framework for Motorcycle Risk Assessment Using Onboard Panoramic Camera (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 44:1-44:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.44
https://doi.org/10.4230/LIPIcs.GIScience.2023.44
Abstract
Traditional safety analysis methods based on historical crash data and simulation models have limitations in capturing real-world driving scenarios. In this experiment, panoramic videos recorded from a motorcyclist’s helmet in Bangkok, Thailand, were narrated using an image-to-text model and then put into a Large Language Model (LLM) to identify potential hazards and assess crash risks. The framework can assess static and moving objects with the potential for early warning and incident analysis. However, the limitations of the existing image-to-text model cause its inability to handle panoramic images effectively.
Subject Classification
ACM Subject Classification
- Information systems → Geographic information systems
- Computing methodologies → Scene understanding
Keywords
- Traffic incident risk
- Large Language Model
- Vision-Language Model
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References
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