A Real-Time Safety-Based Optimal Velocity Model
Modeling safety-critical driver behavior at signalized intersections needs to account for the driver’s planned decision process, where a driver executes a plan to avoid collision in multiple time steps. Such a process can be embedded in the Optimal Velocity Model (OVM) that traditionally...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9697070/ |
| _version_ | 1797974381803077632 |
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| author | Awad Abdelhalim Montasir Abbas |
| author_facet | Awad Abdelhalim Montasir Abbas |
| author_sort | Awad Abdelhalim |
| collection | DOAJ |
| description | Modeling safety-critical driver behavior at signalized intersections needs to account for the driver’s planned decision process, where a driver executes a plan to avoid collision in multiple time steps. Such a process can be embedded in the Optimal Velocity Model (OVM) that traditionally assumes that drivers base their “mental intention” on a distance gap only. We propose and evaluate a data-driven OVM based on real-time inference of roadside traffic video data. First, we extract vehicle trajectory data from roadside traffic footage through our advanced video processing algorithm (VT-Lane) for a study site in Blacksburg, VA, USA. Vehicles engaged in car-following episodes are then identified within the extracted vehicle trajectories database, and the real-time time-to-collision (TTC) is calculated for all car-following instances. Then, we analyze the driver behavior to predict the shape of the underlying TTC-based desired velocity function. A clustering approach is used to assess car-following behavior heterogeneity and understand the reasons behind outlying driving behaviors at the intersection to design our model accordingly. The results of this assessment show that the calibrated TTC-based OVM can replicate the observed driving behavior by capturing the acceleration pattern with an error 20% lower than the gap distance-based OVM. |
| first_indexed | 2024-04-11T04:19:07Z |
| format | Article |
| id | doaj.art-11c3bd209f524cb1b73aa79bc7685984 |
| institution | Directory Open Access Journal |
| issn | 2687-7813 |
| language | English |
| last_indexed | 2024-04-11T04:19:07Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj.art-11c3bd209f524cb1b73aa79bc76859842022-12-31T00:01:58ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132022-01-01316517510.1109/OJITS.2022.31477449697070A Real-Time Safety-Based Optimal Velocity ModelAwad Abdelhalim0https://orcid.org/0000-0002-6384-7765Montasir Abbas1https://orcid.org/0000-0002-9938-0255The Charles Edward Via, Jr. Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USAThe Charles Edward Via, Jr. Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USAModeling safety-critical driver behavior at signalized intersections needs to account for the driver’s planned decision process, where a driver executes a plan to avoid collision in multiple time steps. Such a process can be embedded in the Optimal Velocity Model (OVM) that traditionally assumes that drivers base their “mental intention” on a distance gap only. We propose and evaluate a data-driven OVM based on real-time inference of roadside traffic video data. First, we extract vehicle trajectory data from roadside traffic footage through our advanced video processing algorithm (VT-Lane) for a study site in Blacksburg, VA, USA. Vehicles engaged in car-following episodes are then identified within the extracted vehicle trajectories database, and the real-time time-to-collision (TTC) is calculated for all car-following instances. Then, we analyze the driver behavior to predict the shape of the underlying TTC-based desired velocity function. A clustering approach is used to assess car-following behavior heterogeneity and understand the reasons behind outlying driving behaviors at the intersection to design our model accordingly. The results of this assessment show that the calibrated TTC-based OVM can replicate the observed driving behavior by capturing the acceleration pattern with an error 20% lower than the gap distance-based OVM.https://ieeexplore.ieee.org/document/9697070/Driver behavior calibrationintersection safetyoptimal velocity modelvehicle trajectory tracking |
| spellingShingle | Awad Abdelhalim Montasir Abbas A Real-Time Safety-Based Optimal Velocity Model IEEE Open Journal of Intelligent Transportation Systems Driver behavior calibration intersection safety optimal velocity model vehicle trajectory tracking |
| title | A Real-Time Safety-Based Optimal Velocity Model |
| title_full | A Real-Time Safety-Based Optimal Velocity Model |
| title_fullStr | A Real-Time Safety-Based Optimal Velocity Model |
| title_full_unstemmed | A Real-Time Safety-Based Optimal Velocity Model |
| title_short | A Real-Time Safety-Based Optimal Velocity Model |
| title_sort | real time safety based optimal velocity model |
| topic | Driver behavior calibration intersection safety optimal velocity model vehicle trajectory tracking |
| url | https://ieeexplore.ieee.org/document/9697070/ |
| work_keys_str_mv | AT awadabdelhalim arealtimesafetybasedoptimalvelocitymodel AT montasirabbas arealtimesafetybasedoptimalvelocitymodel AT awadabdelhalim realtimesafetybasedoptimalvelocitymodel AT montasirabbas realtimesafetybasedoptimalvelocitymodel |