VT-Lane: An Exploratory Study of an Ad-hoc Framework for Real-time Intersection Turn Count and Trajectory Reconstruction Using NEMA Phases-Based Virtual Traffic Lanes

Abdelhalim, Awad; Abbas, Montasir

doi:10.1109/itsc45102.2020.9294395

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…In a recent study (2), we utilized an extended VT-Lane framework to obtain the trajectories and calibrate a data-driven safety-based driver behavior model for the area of study. Previous works detail the implementation and evaluation of the computer-vision-based trajectory tracking framework (18)(19)(20). Figure 1 shows the area of this study from the perspective of the roadside camera used to obtain the video data, illustrating the NEMA movement enumeration for the site.…”

Section: Methodology Area Of Study Data Collection and The Vissim Modelmentioning

confidence: 99%

An Assessment of Safety-Based Driver Behavior Modeling in Microscopic Simulation Utilizing Real-Time Vehicle Trajectories

Abdelhalim¹,

Abbas²

2022

Preprint

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Accurate representation of observed driving behavior is critical for effectively evaluating safety and performance interventions in simulation modeling. In this study, we implement and evaluate a safety-based Optimal Velocity Model (OVM) to provide a high-fidelity replication of safety-critical behavior in microscopic simulation and showcase its implications for safety-focused assessments of traffic control strategies. A comprehensive simulation model is created for the site of study in PTV VISSIM utilizing detailed vehicle trajectory information extracted from real-time video inference, which are also used to calibrate the parameters of the safety-based OVM to replicate the observed driving behavior in the site of study. The calibrated model is then incorporated as an external driver model that overtakes VISSIM's default Wiedemann 74 model during simulated car-following episodes. The results of the preliminary analysis show the significant improvements achieved by using our model in replicating the existing safety conflicts observed at the site of the study. We then utilize this improved representation of the status quo to assess the potential impact of different scenarios of signal control and speed limit enforcement in reducing those existing conflicts by up to 23%. The results of this study showcase the considerable improvements that can be achieved by utilizing data-driven car-following behavior modeling, and the workflow presented provides an end-to-end, scalable, automated, and generalizable approach for replicating the existing driving behavior observed at a site of interest in microscopic simulation by utilizing vehicle trajectories efficiently extracted via roadside video inference.

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Section: Methodology Area Of Study Data Collection and The Vissim Modelmentioning

confidence: 99%

An Assessment of Safety-Based Driver Behavior Modeling in Microscopic Simulation Utilizing Real-Time Vehicle Trajectories

Abdelhalim¹,

Abbas²

2022

Preprint

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“…We then introduced the concept of NEMA phases-based virtual traffic lanes to obtain vehicle turn movement counts and address issues of vehicle identity switching which results from occlusion. The base of the framework is detailed and evaluated in [35] and [36]. The method used for reference object scaling, distance and speed estimation is discussed and assessed in detail in [37].…”

Section: A Previous Workmentioning

confidence: 99%

A Real-Time Safety-Based Optimal Velocity Model

Abdelhalim

Abbas

2022

IEEE Open J. Intell. Transp. Syst.

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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.

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“…Accurate and efficient speed estimation remains a challenging task, especially from low-altitude roadside cameras where methods utilizing license plate tracking and deep neural nets outperform those based on vehicle tracking due to detection instability and frequent identity switches. Given the proven ability of our VT-Lane vehicle tracking framework to provide accurate vehicle trajectories in real-time while resolving vehicle identity switches due to occlusion [24], [25], the authors believe there is a high potential for speed estimation with reliable accuracy from the produced trajectories without the need for additional complex deep neural nets or the privacy concerns associated with license plate tracking.…”

Section: B the Use Of Object Detection And Tracking In Vehicle Speed ...mentioning

confidence: 99%

“…For the base object detection and tracking task in VT-Lane, we implemented a combination of YOLO v4 [6] and Deep-SORT [26]. Our complete framework is detailed and assessed in [24]. The proposed framework has proven efficient in trajectory tracking, turn movement classification, and trajectory reconstruction after effectively handling identity switches.…”

Section: A Vehicle Tracking and Movement Classification Frameworkmentioning

confidence: 99%

A Framework for Real-time Traffic Trajectory Tracking, Speed Estimation, and Driver Behavior Calibration at Urban Intersections Using Virtual Traffic Lanes

Abdelhalim

Abbas

Kotha

et al. 2021

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

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In a previous study, we presented VT-Lane, a three-step framework for real-time vehicle detection, tracking, and turn movement classification at urban intersections. In this study, we present a case study incorporating the highly accurate trajectories and movement classification obtained via VT-Lane for the purpose of speed estimation and driver behavior calibration for traffic at urban intersections. First, we use a highly instrumented vehicle to verify the estimated speeds obtained from video inference. The results of the speed validation show that our method can estimate the average travel speed of detected vehicles in real-time with an error of 0.19 m/sec, which is equivalent to 2% of the average observed travel speeds in the intersection of study. Instantaneous speeds (at the resolution of 30 Hz) were found to be estimated with an average error of 0.21 m/sec and 0.86 m/sec respectively for free flowing and congested traffic conditions. We then use the estimated speeds to calibrate the parameters of a driver behavior model for the vehicles in the area of study. The results show that the calibrated model replicates the driving behavior with an average error of 0.45 m/sec, indicating the high potential for using this framework for automated, largescale calibration of car-following models from roadside traffic video data, which can lead to substantial improvements in traffic modeling via microscopic simulation.

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VT-Lane: An Exploratory Study of an Ad-hoc Framework for Real-time Intersection Turn Count and Trajectory Reconstruction Using NEMA Phases-Based Virtual Traffic Lanes

Cited by 4 publications

References 23 publications

An Assessment of Safety-Based Driver Behavior Modeling in Microscopic Simulation Utilizing Real-Time Vehicle Trajectories

An Assessment of Safety-Based Driver Behavior Modeling in Microscopic Simulation Utilizing Real-Time Vehicle Trajectories

A Real-Time Safety-Based Optimal Velocity Model

A Framework for Real-time Traffic Trajectory Tracking, Speed Estimation, and Driver Behavior Calibration at Urban Intersections Using Virtual Traffic Lanes

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