A novel merging strategy model considering the remaining distance in the acceleration lane
Abstract The driver of a merging vehicle must account for both the lane‐changing risk and the remaining distance in the acceleration lane (RD) during a merging decision‐making process. To investigate the impact of RD on merge decisions, a typical freeway merging section was selected and monitored wi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-09-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12381 |
| _version_ | 1797680770553217024 |
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| author | Menglu Gu Yanqi Su Chang Wang Rui Fu Yingshi Guo Wei Yuan |
| author_facet | Menglu Gu Yanqi Su Chang Wang Rui Fu Yingshi Guo Wei Yuan |
| author_sort | Menglu Gu |
| collection | DOAJ |
| description | Abstract The driver of a merging vehicle must account for both the lane‐changing risk and the remaining distance in the acceleration lane (RD) during a merging decision‐making process. To investigate the impact of RD on merge decisions, a typical freeway merging section was selected and monitored with a millimetre radar and a high‐resolution digital camera, which were mounted on the guardrail in the gore area. More than 2000 merging vehicles were captured during the data collection process. The effects of the surrounding vehicles on the merging behaviour were analyzed, and a merging strategy model considering RD that was based on the random forest algorithm was constructed. The results show that the following vehicle in the target lane is the main factor that affects the merging behaviour of the merging vehicle. When the decision time window was set to 0.6 s, the proposed merging decision model could distinguish ‘Merge’ events and ‘Wait’ events with accuracies of 97.2% and 89.4%, respectively. The overall accuracy of the model was 94.9%, which was 3.9% higher than for a corresponding merging decision model that excluded RD influence. The proposed merging decision model can aid merging processes and give cues for human‐like merge decisions of automated vehicles. |
| first_indexed | 2024-03-11T23:35:01Z |
| format | Article |
| id | doaj.art-8e00ec0268ef492a97e67cb21831f35b |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-03-11T23:35:01Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-8e00ec0268ef492a97e67cb21831f35b2023-09-20T04:44:26ZengWileyIET Intelligent Transport Systems1751-956X1751-95782023-09-011791879189010.1049/itr2.12381A novel merging strategy model considering the remaining distance in the acceleration laneMenglu Gu0Yanqi Su1Chang Wang2Rui Fu3Yingshi Guo4Wei Yuan5School of Automobile Chang'an University Xi'an ChinaSchool of Automobile Chang'an University Xi'an ChinaSchool of Automobile Chang'an University Xi'an ChinaSchool of Automobile Chang'an University Xi'an ChinaSchool of Automobile Chang'an University Xi'an ChinaSchool of Automobile Chang'an University Xi'an ChinaAbstract The driver of a merging vehicle must account for both the lane‐changing risk and the remaining distance in the acceleration lane (RD) during a merging decision‐making process. To investigate the impact of RD on merge decisions, a typical freeway merging section was selected and monitored with a millimetre radar and a high‐resolution digital camera, which were mounted on the guardrail in the gore area. More than 2000 merging vehicles were captured during the data collection process. The effects of the surrounding vehicles on the merging behaviour were analyzed, and a merging strategy model considering RD that was based on the random forest algorithm was constructed. The results show that the following vehicle in the target lane is the main factor that affects the merging behaviour of the merging vehicle. When the decision time window was set to 0.6 s, the proposed merging decision model could distinguish ‘Merge’ events and ‘Wait’ events with accuracies of 97.2% and 89.4%, respectively. The overall accuracy of the model was 94.9%, which was 3.9% higher than for a corresponding merging decision model that excluded RD influence. The proposed merging decision model can aid merging processes and give cues for human‐like merge decisions of automated vehicles.https://doi.org/10.1049/itr2.12381driving aidmerging strategyrandom forest algorithmremaining distance in the acceleration lane |
| spellingShingle | Menglu Gu Yanqi Su Chang Wang Rui Fu Yingshi Guo Wei Yuan A novel merging strategy model considering the remaining distance in the acceleration lane IET Intelligent Transport Systems driving aid merging strategy random forest algorithm remaining distance in the acceleration lane |
| title | A novel merging strategy model considering the remaining distance in the acceleration lane |
| title_full | A novel merging strategy model considering the remaining distance in the acceleration lane |
| title_fullStr | A novel merging strategy model considering the remaining distance in the acceleration lane |
| title_full_unstemmed | A novel merging strategy model considering the remaining distance in the acceleration lane |
| title_short | A novel merging strategy model considering the remaining distance in the acceleration lane |
| title_sort | novel merging strategy model considering the remaining distance in the acceleration lane |
| topic | driving aid merging strategy random forest algorithm remaining distance in the acceleration lane |
| url | https://doi.org/10.1049/itr2.12381 |
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