Influential Control Parameters for Autonomous Vehicles in a Mixed Environment
Autonomous vehicles will be widely operated on roadways in the near future. Prior to the broad adoption of autonomous vehicles (AVs), conventional human-driven vehicles would coexist with their AVs counterparts on the same roads, resulting in traffic scenarios that had never been observed before. On...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10596678/ |
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| author | Hossam M. Abdelghaffar Monica Menendez |
| author_facet | Hossam M. Abdelghaffar Monica Menendez |
| author_sort | Hossam M. Abdelghaffar |
| collection | DOAJ |
| description | Autonomous vehicles will be widely operated on roadways in the near future. Prior to the broad adoption of autonomous vehicles (AVs), conventional human-driven vehicles would coexist with their AVs counterparts on the same roads, resulting in traffic scenarios that had never been observed before. One such scenario involves the merging of AVs onto a main road. This study assesses the effects of incorporating AVs into a transportation system at different levels of AV penetration. This research analyzes AVs' influence by examining performance metrics such as travel time, delay, number of stops, and stop delay. The results demonstrate that introducing AVs at penetration rates of 10%, 25%, and 50% leads to an average total network delay increase of 4%, 7%, and 18%, respectively. A variety of parameters influence AV performance. To improve AV performance and, consequently, performance metrics, it is critical to identify and effectively control the influential parameters that have a significant impact on AV performance. Consequently, in this paper, we employ the quasi-optimized trajectory elementary effect sensitivity analysis approach, to identify the parameters whose variations are anticipated to significantly impact the performance metrics. The research findings reveal that the time gap, standstill distance, acceleration from a standstill, and the following distance oscillation are all influential parameters affecting the performance metrics of the network, the merging road, and the main road at various levels of AV penetration rate. |
| format | Article |
| id | doaj-art-ef27caecbe8241318f4e0dc29eda940f |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-ef27caecbe8241318f4e0dc29eda940f2025-01-30T00:04:08ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-01592793910.1109/OJVT.2024.342698910596678Influential Control Parameters for Autonomous Vehicles in a Mixed EnvironmentHossam M. Abdelghaffar0https://orcid.org/0000-0003-4396-5913Monica Menendez1https://orcid.org/0000-0001-5701-0523Division of Engineering, New York University Abu Dhabi, Abu Dhabi, UAEDivision of Engineering, New York University Abu Dhabi, Abu Dhabi, UAEAutonomous vehicles will be widely operated on roadways in the near future. Prior to the broad adoption of autonomous vehicles (AVs), conventional human-driven vehicles would coexist with their AVs counterparts on the same roads, resulting in traffic scenarios that had never been observed before. One such scenario involves the merging of AVs onto a main road. This study assesses the effects of incorporating AVs into a transportation system at different levels of AV penetration. This research analyzes AVs' influence by examining performance metrics such as travel time, delay, number of stops, and stop delay. The results demonstrate that introducing AVs at penetration rates of 10%, 25%, and 50% leads to an average total network delay increase of 4%, 7%, and 18%, respectively. A variety of parameters influence AV performance. To improve AV performance and, consequently, performance metrics, it is critical to identify and effectively control the influential parameters that have a significant impact on AV performance. Consequently, in this paper, we employ the quasi-optimized trajectory elementary effect sensitivity analysis approach, to identify the parameters whose variations are anticipated to significantly impact the performance metrics. The research findings reveal that the time gap, standstill distance, acceleration from a standstill, and the following distance oscillation are all influential parameters affecting the performance metrics of the network, the merging road, and the main road at various levels of AV penetration rate.https://ieeexplore.ieee.org/document/10596678/Autonomous vehicleinfluential control input parametermixed traffic environment |
| spellingShingle | Hossam M. Abdelghaffar Monica Menendez Influential Control Parameters for Autonomous Vehicles in a Mixed Environment IEEE Open Journal of Vehicular Technology Autonomous vehicle influential control input parameter mixed traffic environment |
| title | Influential Control Parameters for Autonomous Vehicles in a Mixed Environment |
| title_full | Influential Control Parameters for Autonomous Vehicles in a Mixed Environment |
| title_fullStr | Influential Control Parameters for Autonomous Vehicles in a Mixed Environment |
| title_full_unstemmed | Influential Control Parameters for Autonomous Vehicles in a Mixed Environment |
| title_short | Influential Control Parameters for Autonomous Vehicles in a Mixed Environment |
| title_sort | influential control parameters for autonomous vehicles in a mixed environment |
| topic | Autonomous vehicle influential control input parameter mixed traffic environment |
| url | https://ieeexplore.ieee.org/document/10596678/ |
| work_keys_str_mv | AT hossammabdelghaffar influentialcontrolparametersforautonomousvehiclesinamixedenvironment AT monicamenendez influentialcontrolparametersforautonomousvehiclesinamixedenvironment |