Construction of Collision-Type Prediction Models Based on Pre-crash Data for Advanced Driver Assistance Systems
ABSTRACT: Collision-type prediction models based on pre-crash information are important because there is a relationship between collision type, avoidance operations, and occupant injuries. Thus, they can be applied to autonomous driving systems (ADS) or advanced driver assistance systems (ADAS) to p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Society of Automotive Engineers of Japan, Inc.
2022-10-01
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| Series: | International Journal of Automotive Engineering |
| Online Access: | https://www.jstage.jst.go.jp/article/jsaeijae/13/4/13_20224547/_article/-char/ja |
| _version_ | 1827325400788762624 |
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| author | Wei Junhao Yusuke Miyazaki Koji Kitamura Fusako Sato |
| author_facet | Wei Junhao Yusuke Miyazaki Koji Kitamura Fusako Sato |
| author_sort | Wei Junhao |
| collection | DOAJ |
| description | ABSTRACT: Collision-type prediction models based on pre-crash information are important because there is a relationship between collision type, avoidance operations, and occupant injuries. Thus, they can be applied to autonomous driving systems (ADS) or advanced driver assistance systems (ADAS) to prevent serious accidents or minimize damage during collisions. In this study, we investigated the application of collision-type prediction models based on several machine learning methods and compared their performance to determine the best model based on their f1 scores. The results revealed that the light gradient boosting machine (LGBM) model had a high f1 score that exceeded 0.92, which implied that it could potentially be used for ADS and ADAS applications. Furthermore, a brief analysis was performed on the ranking of various factors, which provided useful insight into the significance of several pre-crash factors and their distributions. |
| first_indexed | 2024-03-07T14:27:34Z |
| format | Article |
| id | doaj.art-e97a187620b440b7be4e07540bc3b3b9 |
| institution | Directory Open Access Journal |
| issn | 2185-0992 |
| language | English |
| last_indexed | 2024-03-07T14:27:34Z |
| publishDate | 2022-10-01 |
| publisher | Society of Automotive Engineers of Japan, Inc. |
| record_format | Article |
| series | International Journal of Automotive Engineering |
| spelling | doaj.art-e97a187620b440b7be4e07540bc3b3b92024-03-06T06:56:18ZengSociety of Automotive Engineers of Japan, Inc.International Journal of Automotive Engineering2185-09922022-10-0113416316810.20485/jsaeijae.13.4_163Construction of Collision-Type Prediction Models Based on Pre-crash Data for Advanced Driver Assistance SystemsWei Junhao0Yusuke Miyazaki1Koji Kitamura2Fusako Sato3Tokyo Institute of TechnologyTokyo Institute of TechnologyNational Institute of Advanced Industrial Science and TechnologyJapan Automobile Research InstituteABSTRACT: Collision-type prediction models based on pre-crash information are important because there is a relationship between collision type, avoidance operations, and occupant injuries. Thus, they can be applied to autonomous driving systems (ADS) or advanced driver assistance systems (ADAS) to prevent serious accidents or minimize damage during collisions. In this study, we investigated the application of collision-type prediction models based on several machine learning methods and compared their performance to determine the best model based on their f1 scores. The results revealed that the light gradient boosting machine (LGBM) model had a high f1 score that exceeded 0.92, which implied that it could potentially be used for ADS and ADAS applications. Furthermore, a brief analysis was performed on the ranking of various factors, which provided useful insight into the significance of several pre-crash factors and their distributions.https://www.jstage.jst.go.jp/article/jsaeijae/13/4/13_20224547/_article/-char/ja |
| spellingShingle | Wei Junhao Yusuke Miyazaki Koji Kitamura Fusako Sato Construction of Collision-Type Prediction Models Based on Pre-crash Data for Advanced Driver Assistance Systems International Journal of Automotive Engineering |
| title | Construction of Collision-Type Prediction Models Based on Pre-crash Data for Advanced Driver Assistance Systems |
| title_full | Construction of Collision-Type Prediction Models Based on Pre-crash Data for Advanced Driver Assistance Systems |
| title_fullStr | Construction of Collision-Type Prediction Models Based on Pre-crash Data for Advanced Driver Assistance Systems |
| title_full_unstemmed | Construction of Collision-Type Prediction Models Based on Pre-crash Data for Advanced Driver Assistance Systems |
| title_short | Construction of Collision-Type Prediction Models Based on Pre-crash Data for Advanced Driver Assistance Systems |
| title_sort | construction of collision type prediction models based on pre crash data for advanced driver assistance systems |
| url | https://www.jstage.jst.go.jp/article/jsaeijae/13/4/13_20224547/_article/-char/ja |
| work_keys_str_mv | AT weijunhao constructionofcollisiontypepredictionmodelsbasedonprecrashdataforadvanceddriverassistancesystems AT yusukemiyazaki constructionofcollisiontypepredictionmodelsbasedonprecrashdataforadvanceddriverassistancesystems AT kojikitamura constructionofcollisiontypepredictionmodelsbasedonprecrashdataforadvanceddriverassistancesystems AT fusakosato constructionofcollisiontypepredictionmodelsbasedonprecrashdataforadvanceddriverassistancesystems |