Modeling Lane-Changing Behaviors for Autonomous Vehicles Based on Vehicle-to-Vehicle Communication
With the advent of autonomous vehicles (AVs) and advanced driving assistance systems (ADAS), there has been a growing interest in studying driving behaviors within the field of transportation science. Given that the transition period of mixed traffic is expected to continue for more than 30 years, i...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10264101/ |
| _version_ | 1797663059210141696 |
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| author | Euntak Lee Youngjun Han Ju-Yeon Lee Bongsoo Son |
| author_facet | Euntak Lee Youngjun Han Ju-Yeon Lee Bongsoo Son |
| author_sort | Euntak Lee |
| collection | DOAJ |
| description | With the advent of autonomous vehicles (AVs) and advanced driving assistance systems (ADAS), there has been a growing interest in studying driving behaviors within the field of transportation science. Given that the transition period of mixed traffic is expected to continue for more than 30 years, it is crucial to evolve AV technology to resemble human driving, especially in the freeway weaving sections. Lane-changing (LC) maneuvers in these sections could cause problems for traffic flow, such as traffic breakdown, oscillation, or bottleneck activation. This study proposes an interpretable LC implementation model for naturalistic driving behaviors of AVs based on vehicle-to-vehicle (V2V) communication. To achieve this objective, a systematic selection process is adopted to find optimal V2V features that resemble how human drivers assess LC situations. Based on the minimum redundancy maximum relevance (mRMR) algorithm, seven V2V features have been selected out of 25 candidates. Then, a support vector machine (SVM) is employed to investigate how these features exhibit in each of LC and lane-keeping (LK) situations. The proposed model was applied in a field case of a weaving Section on freeway US 101. Performance measures of simple accuracy, precision, recall, and F1-score show high accuracy of 0.9814, 0.9150, 0.7955, and 0.8511, respectively. Subsequently, a strategy for naturalistic LC behaviors of AVs was simulated. The proposed model outperforms high prediction accuracy compared to other existing models. Particularly, errors in the lateral movements have significantly improved. These results suggest that the proposed model effectively simulates naturalistic LC behaviors based on V2V communication. |
| first_indexed | 2024-03-11T19:09:04Z |
| format | Article |
| id | doaj.art-4ba3931b24a94dee99e8546670b64001 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T19:09:04Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4ba3931b24a94dee99e8546670b640012023-10-09T23:00:45ZengIEEEIEEE Access2169-35362023-01-011110799710801010.1109/ACCESS.2023.331955010264101Modeling Lane-Changing Behaviors for Autonomous Vehicles Based on Vehicle-to-Vehicle CommunicationEuntak Lee0https://orcid.org/0000-0002-2575-0196Youngjun Han1https://orcid.org/0000-0003-4764-691XJu-Yeon Lee2Bongsoo Son3Department of Urban Planning and Engineering, Yonsei University, Seoul, South KoreaThe Seoul Institute, Seoul, South KoreaKorea Transport Institute, Sejong, South KoreaDepartment of Urban Planning and Engineering, Yonsei University, Seoul, South KoreaWith the advent of autonomous vehicles (AVs) and advanced driving assistance systems (ADAS), there has been a growing interest in studying driving behaviors within the field of transportation science. Given that the transition period of mixed traffic is expected to continue for more than 30 years, it is crucial to evolve AV technology to resemble human driving, especially in the freeway weaving sections. Lane-changing (LC) maneuvers in these sections could cause problems for traffic flow, such as traffic breakdown, oscillation, or bottleneck activation. This study proposes an interpretable LC implementation model for naturalistic driving behaviors of AVs based on vehicle-to-vehicle (V2V) communication. To achieve this objective, a systematic selection process is adopted to find optimal V2V features that resemble how human drivers assess LC situations. Based on the minimum redundancy maximum relevance (mRMR) algorithm, seven V2V features have been selected out of 25 candidates. Then, a support vector machine (SVM) is employed to investigate how these features exhibit in each of LC and lane-keeping (LK) situations. The proposed model was applied in a field case of a weaving Section on freeway US 101. Performance measures of simple accuracy, precision, recall, and F1-score show high accuracy of 0.9814, 0.9150, 0.7955, and 0.8511, respectively. Subsequently, a strategy for naturalistic LC behaviors of AVs was simulated. The proposed model outperforms high prediction accuracy compared to other existing models. Particularly, errors in the lateral movements have significantly improved. These results suggest that the proposed model effectively simulates naturalistic LC behaviors based on V2V communication.https://ieeexplore.ieee.org/document/10264101/Autonomous vehiclesvehicle-to-vehicle communicationlane-changing behavior |
| spellingShingle | Euntak Lee Youngjun Han Ju-Yeon Lee Bongsoo Son Modeling Lane-Changing Behaviors for Autonomous Vehicles Based on Vehicle-to-Vehicle Communication IEEE Access Autonomous vehicles vehicle-to-vehicle communication lane-changing behavior |
| title | Modeling Lane-Changing Behaviors for Autonomous Vehicles Based on Vehicle-to-Vehicle Communication |
| title_full | Modeling Lane-Changing Behaviors for Autonomous Vehicles Based on Vehicle-to-Vehicle Communication |
| title_fullStr | Modeling Lane-Changing Behaviors for Autonomous Vehicles Based on Vehicle-to-Vehicle Communication |
| title_full_unstemmed | Modeling Lane-Changing Behaviors for Autonomous Vehicles Based on Vehicle-to-Vehicle Communication |
| title_short | Modeling Lane-Changing Behaviors for Autonomous Vehicles Based on Vehicle-to-Vehicle Communication |
| title_sort | modeling lane changing behaviors for autonomous vehicles based on vehicle to vehicle communication |
| topic | Autonomous vehicles vehicle-to-vehicle communication lane-changing behavior |
| url | https://ieeexplore.ieee.org/document/10264101/ |
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