Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation
Human drivers can have diverse car-following behaviors when interacting with connected and automated vehicles (CAVs) and other human-driven vehicles in mixed traffic where many human-driven vehicles and a limited number of CAVs frequently interact and share the road. In this study, Inverse Reinforce...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9411849/ |
| _version_ | 1828176474575732736 |
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| author | Mehmet Fatih Ozkan Yao Ma |
| author_facet | Mehmet Fatih Ozkan Yao Ma |
| author_sort | Mehmet Fatih Ozkan |
| collection | DOAJ |
| description | Human drivers can have diverse car-following behaviors when interacting with connected and automated vehicles (CAVs) and other human-driven vehicles in mixed traffic where many human-driven vehicles and a limited number of CAVs frequently interact and share the road. In this study, Inverse Reinforcement Learning (IRL) is used to model unique car-following behaviors of different human drivers when interacting with the CAV and another human-driven vehicle by using their driving demonstrations collected from in-field driving tests. The learned driver behavior model is shown that the personalized driving behaviors accurately and consistently can be characterized when following the different types of preceding vehicles in a variety of traffic situations. Furthermore, the energy efficiency of different human-driven vehicles when interacting with the CAV and the human-driven vehicle is investigated with the heterogeneous characteristics of drivers’ behaviors, considering driving behaviors have significant influences on vehicle fuel economy. A detailed analysis reveals the significant fuel-saving benefits of the CAV to the following human-driven vehicles during the car-following scenario and the extent of such benefits varies among tested human drivers owing to their intrinsic preferences and perception of CAV. These findings suggest that human-CAV interactions can be effectively leveraged to improve the energy efficiency of mixed traffic. |
| first_indexed | 2024-04-12T04:45:15Z |
| format | Article |
| id | doaj.art-f1bde2f94ea94fce8ad3371073bc1950 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T04:45:15Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f1bde2f94ea94fce8ad3371073bc19502022-12-22T03:47:31ZengIEEEIEEE Access2169-35362021-01-019646966470710.1109/ACCESS.2021.30751949411849Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency EvaluationMehmet Fatih Ozkan0https://orcid.org/0000-0002-6506-553XYao Ma1https://orcid.org/0000-0001-6107-7579Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, USADepartment of Mechanical Engineering, Texas Tech University, Lubbock, TX, USAHuman drivers can have diverse car-following behaviors when interacting with connected and automated vehicles (CAVs) and other human-driven vehicles in mixed traffic where many human-driven vehicles and a limited number of CAVs frequently interact and share the road. In this study, Inverse Reinforcement Learning (IRL) is used to model unique car-following behaviors of different human drivers when interacting with the CAV and another human-driven vehicle by using their driving demonstrations collected from in-field driving tests. The learned driver behavior model is shown that the personalized driving behaviors accurately and consistently can be characterized when following the different types of preceding vehicles in a variety of traffic situations. Furthermore, the energy efficiency of different human-driven vehicles when interacting with the CAV and the human-driven vehicle is investigated with the heterogeneous characteristics of drivers’ behaviors, considering driving behaviors have significant influences on vehicle fuel economy. A detailed analysis reveals the significant fuel-saving benefits of the CAV to the following human-driven vehicles during the car-following scenario and the extent of such benefits varies among tested human drivers owing to their intrinsic preferences and perception of CAV. These findings suggest that human-CAV interactions can be effectively leveraged to improve the energy efficiency of mixed traffic.https://ieeexplore.ieee.org/document/9411849/Connected and automated vehiclesdriver behavior modelingfuel economyinverse reinforcement learning |
| spellingShingle | Mehmet Fatih Ozkan Yao Ma Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation IEEE Access Connected and automated vehicles driver behavior modeling fuel economy inverse reinforcement learning |
| title | Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation |
| title_full | Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation |
| title_fullStr | Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation |
| title_full_unstemmed | Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation |
| title_short | Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation |
| title_sort | modeling driver behavior in car following interactions with automated and human driven vehicles and energy efficiency evaluation |
| topic | Connected and automated vehicles driver behavior modeling fuel economy inverse reinforcement learning |
| url | https://ieeexplore.ieee.org/document/9411849/ |
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