A Physics-Based Longitudinal Driver Model for Automated Vehicles
In this study, we develop a physics-based autonomous vehicle longitudinal driver model (PAVL-DM) to move an automated vehicle (AV) forward with a minimum following gap while considering safety and passenger comfort. Unlike existing driver models for longitudinal control, PAVL-DM parameters do not ne...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9840373/ |
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| author | Mizanur Rahman Md Rafiul Islam Mashrur Chowdhury Taufiquar Khan |
| author_facet | Mizanur Rahman Md Rafiul Islam Mashrur Chowdhury Taufiquar Khan |
| author_sort | Mizanur Rahman |
| collection | DOAJ |
| description | In this study, we develop a physics-based autonomous vehicle longitudinal driver model (PAVL-DM) to move an automated vehicle (AV) forward with a minimum following gap while considering safety and passenger comfort. Unlike existing driver models for longitudinal control, PAVL-DM parameters do not need to be calibrated for different traffic states, such as congested and non-congested traffic conditions. First, we present the concept, theoretical considerations and mathematical formulations of the PAVL-DM longitudinal control model for AVs. Second, we theoretically analyze the PAVL-DM model equilibria and robustness for safety assurance and string stability. Third, we present numerical analysis related to riding comfort and a dynamic minimum following gap function for each automated vehicle in a platoon while considering the safety for the automated platoon. Our analyses suggest that the PAVL-DM (i) is able to maintain safety between a subject AV and an immediate front vehicle using a newly defined safe gap function depending on the speed and reaction time of an AV; (ii) shows local stability and string stability; and (iii) provides riding comfort for a range of autonomous driving aggressiveness depending on passengers’ corresponding preferences on driving pattern. In addition, we conducted a case study using a real-world dataset, which proves that an AV with the PAVL-DM model maintains a minimum following gap between a subject AV and an immediate front vehicle without compromising safety and passenger comfort. |
| format | Article |
| id | doaj-art-e5ea5333d45241f78962e24e77399cbc |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e5ea5333d45241f78962e24e77399cbc2025-08-25T23:11:18ZengIEEEIEEE Access2169-35362022-01-0110808838089910.1109/ACCESS.2022.31939979840373A Physics-Based Longitudinal Driver Model for Automated VehiclesMizanur Rahman0https://orcid.org/0000-0003-1128-753XMd Rafiul Islam1Mashrur Chowdhury2https://orcid.org/0000-0002-3275-6983Taufiquar Khan3Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL, USADepartment of Mathematics, Iowa State University, Ames, IA, USAGlenn Department of Civil Engineering, Clemson University, Clemson, SC, USADepartment of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC, USAIn this study, we develop a physics-based autonomous vehicle longitudinal driver model (PAVL-DM) to move an automated vehicle (AV) forward with a minimum following gap while considering safety and passenger comfort. Unlike existing driver models for longitudinal control, PAVL-DM parameters do not need to be calibrated for different traffic states, such as congested and non-congested traffic conditions. First, we present the concept, theoretical considerations and mathematical formulations of the PAVL-DM longitudinal control model for AVs. Second, we theoretically analyze the PAVL-DM model equilibria and robustness for safety assurance and string stability. Third, we present numerical analysis related to riding comfort and a dynamic minimum following gap function for each automated vehicle in a platoon while considering the safety for the automated platoon. Our analyses suggest that the PAVL-DM (i) is able to maintain safety between a subject AV and an immediate front vehicle using a newly defined safe gap function depending on the speed and reaction time of an AV; (ii) shows local stability and string stability; and (iii) provides riding comfort for a range of autonomous driving aggressiveness depending on passengers’ corresponding preferences on driving pattern. In addition, we conducted a case study using a real-world dataset, which proves that an AV with the PAVL-DM model maintains a minimum following gap between a subject AV and an immediate front vehicle without compromising safety and passenger comfort.https://ieeexplore.ieee.org/document/9840373/Car-following modelconnected and automated vehiclescooperative adaptive cruise controlvehicle-to-everything communicationlongitudinal control model |
| spellingShingle | Mizanur Rahman Md Rafiul Islam Mashrur Chowdhury Taufiquar Khan A Physics-Based Longitudinal Driver Model for Automated Vehicles IEEE Access Car-following model connected and automated vehicles cooperative adaptive cruise control vehicle-to-everything communication longitudinal control model |
| title | A Physics-Based Longitudinal Driver Model for Automated Vehicles |
| title_full | A Physics-Based Longitudinal Driver Model for Automated Vehicles |
| title_fullStr | A Physics-Based Longitudinal Driver Model for Automated Vehicles |
| title_full_unstemmed | A Physics-Based Longitudinal Driver Model for Automated Vehicles |
| title_short | A Physics-Based Longitudinal Driver Model for Automated Vehicles |
| title_sort | physics based longitudinal driver model for automated vehicles |
| topic | Car-following model connected and automated vehicles cooperative adaptive cruise control vehicle-to-everything communication longitudinal control model |
| url | https://ieeexplore.ieee.org/document/9840373/ |
| work_keys_str_mv | AT mizanurrahman aphysicsbasedlongitudinaldrivermodelforautomatedvehicles AT mdrafiulislam aphysicsbasedlongitudinaldrivermodelforautomatedvehicles AT mashrurchowdhury aphysicsbasedlongitudinaldrivermodelforautomatedvehicles AT taufiquarkhan aphysicsbasedlongitudinaldrivermodelforautomatedvehicles AT mizanurrahman physicsbasedlongitudinaldrivermodelforautomatedvehicles AT mdrafiulislam physicsbasedlongitudinaldrivermodelforautomatedvehicles AT mashrurchowdhury physicsbasedlongitudinaldrivermodelforautomatedvehicles AT taufiquarkhan physicsbasedlongitudinaldrivermodelforautomatedvehicles |