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Explanation of Traffic Risks with LLM Using GIS Data and Street Images
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- MIMURA Ryota
- Honda R&D Co., Ltd.
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- SHIMOMURA Kota
- Elith Inc. Chubu University
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- ISHIKAWA Atsuya
- Honda R&D Co., Ltd.
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- ITO Osamu
- Honda R&D Co., Ltd.
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- OHMORI Kazuaki
- Elith Inc.
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- SHIMOGAUCHI Ryuta
- Elith Inc.
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- WAKABAYASHI Reoto
- Elith Inc.
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- INOUE Koki
- Elith Inc.
Bibliographic Information
- Other Title
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- GIS データと街路画像を用いたLLMによる交通リスクの説明
Description
<p>Consideration of traffic risk in driver assistance systems and automated driving technology is important in preventing traffic accidents. Traffic risks are considered to be contained in image information. However, it is difficult to explain traffic risk in driving scenes from image information alone, and research in this area has not yet progressed sufficiently. In this study, we propose a multimodal framework that can explain traffic risks by using GIS data and street images. This framework identifies the coordinates of high-risk areas from traffic accident risk maps created based on GIS data and trains a multimodal network using street images associated with those areas. By doing so, we construct a framework that effectively explains traffic risk in an arbitrary scene. Experimental results show that the proposed framework can generate captions that explain traffic risks for high-risk areas based on GIS data.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2024 (0), 1D5GS1003-1D5GS1003, 2024
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390300446018674816
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- ISSN
- 27587347
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed