| Item type |
SIG Technical Reports(1) |
| 公開日 |
2020-02-10 |
| タイトル |
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|
タイトル |
Implementing an Automatic Road Accident Report System with an Accident Simulator |
| タイトル |
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言語 |
en |
|
タイトル |
Implementing an Automatic Road Accident Report System with an Accident Simulator |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
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資源タイプ |
technical report |
| 著者所属 |
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Department of Computer Science, Graduate School of Engineering - Nagoya Institute of Technology |
| 著者所属 |
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Department of Computer Science, Graduate School of Engineering - Nagoya Institute of Technology |
| 著者所属 |
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Department of Computer Science, Graduate School of Engineering - Nagoya Institute of Technology |
| 著者所属(英) |
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en |
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Department of Computer Science, Graduate School of Engineering - Nagoya Institute of Technology |
| 著者所属(英) |
|
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|
en |
|
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Department of Computer Science, Graduate School of Engineering - Nagoya Institute of Technology |
| 著者所属(英) |
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|
en |
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Department of Computer Science, Graduate School of Engineering - Nagoya Institute of Technology |
| 著者名 |
Helton, Agbewonou Yawovi
Tadachika, Ozono
Toramatsu, Shintani
|
| 著者名(英) |
Helton, Agbewonou Yawovi
Tadachika, Ozono
Toramatsu, Shintani
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| 論文抄録 |
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内容記述タイプ |
Other |
|
内容記述 |
The sad side effect of the increasing number of motorized vehicles is the increasing number of road accidents. Road accidents, nowadays, represent a big challenge for all countries in the world. Every year, people conduct lot of researches in AI, Machine Learning and Deep Learning to find efficient solutions to reduce road accidents through predictions thanks to the usage of data from previously occurred road accidents. After an accident occurred, police have to make investigation to know the circumstances of the incident and determine the responsibilities of each actor. To help police to go faster and quicker in these tasks, support systems are requested. Our goal is to create a system that can learn and make work easier and quicker for police and improve the traditional and manual way to determine responsibilities when road accidents occur. In our research, we focused on a system that can automatically build, thanks to a simulator, a 3D simulation (for visualization purpose) of an accident, given as input a manually made accident report. Our simulator, then, automatically generates labeled training data that will be used, later, by the system for image recognition task to predict the responsibilities of each actor in the accident using a custom trained YOLO model and the library Open CV. As a final result, the simulator generates a sketch of the accident to append to the manually made accident report inputted into the system by the user. |
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
The sad side effect of the increasing number of motorized vehicles is the increasing number of road accidents. Road accidents, nowadays, represent a big challenge for all countries in the world. Every year, people conduct lot of researches in AI, Machine Learning and Deep Learning to find efficient solutions to reduce road accidents through predictions thanks to the usage of data from previously occurred road accidents. After an accident occurred, police have to make investigation to know the circumstances of the incident and determine the responsibilities of each actor. To help police to go faster and quicker in these tasks, support systems are requested. Our goal is to create a system that can learn and make work easier and quicker for police and improve the traditional and manual way to determine responsibilities when road accidents occur. In our research, we focused on a system that can automatically build, thanks to a simulator, a 3D simulation (for visualization purpose) of an accident, given as input a manually made accident report. Our simulator, then, automatically generates labeled training data that will be used, later, by the system for image recognition task to predict the responsibilities of each actor in the accident using a custom trained YOLO model and the library Open CV. As a final result, the simulator generates a sketch of the accident to append to the manually made accident report inputted into the system by the user. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AA11135936 |
| 書誌情報 |
研究報告知能システム（ICS）
巻 2020-ICS-197,
号 9,
p. 1-4,
発行日 2020-02-10
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-885X |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-ICS20197009.pdf (1.4 MB)
