Autonomous and connected intersection crossing traffic management using discrete-time occupancies trajectory

Qiang, Lu; Kim, Kyoung-Dae

doi:10.1007/s10489-018-1357-1

Cited by 22 publications

(15 citation statements)

References 33 publications

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“…e data analysis results in Table 1 further indicate that the correlation between the jerk e ect and driving behavior increases by 50.6% on average compared to that of an RV since V2V communication technology provides instant information regarding the interactions between vehicles [33,36,37]. is nding may improve the stability of connected tra c ow.…”

Section: Data Mining Analysismentioning

confidence: 97%

Analysis of V2V Messages for Car-Following Behavior with the Traffic Jerk Effect

Hui

Liu

et al. 2020

Journal of Advanced Transportation

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The existing model of sudden acceleration changes, referred to as the traffic jerk effect, is mostly based on theoretical hypotheses, and previous research has mainly focused on traditional traffic flow. To this end, this paper investigates the change in the traffic jerk effect between inactive and active vehicle-to-vehicle (V2V) communications based on field experimental data. Data mining results show that the correlation between the jerk effect and the driving behavior increases by 50.6% on average when V2V messages are received. In light of the data analysis results, a new car-following model is proposed to explore the jerk effect in a connected environment. The model parameters are calibrated, and the results show that the standard deviation between the new model simulation data and the observed data decreases by 38.2% compared to that of the full velocity difference (FVD) model. Linear and nonlinear analyses of the calibrated model are then carried out to evaluate the connected traffic flow stability. Finally, the theoretical analysis is verified by simulation experiments. Both the theoretical and simulation results show that the headway amplitude and velocity fluctuations are reduced when considering the jerk effect in a connected environment, and the traffic flow stability is improved.

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Section: Data Mining Analysismentioning

confidence: 97%

Analysis of V2V Messages for Car-Following Behavior with the Traffic Jerk Effect

Hui

Liu

et al. 2020

Journal of Advanced Transportation

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“…Other methods proposed in the previous studies [6][7][8][9] do not discretize the intersection. In Yan et al's study, 6 they identified the compatible trajectories of the vehicles in the intersection which are the trajectories that are not in conflict with each other.…”

Section: Related Workmentioning

confidence: 99%

“…The vehicles communicate with each other to broadcast their upcoming entrances into the intersection, and the scheme has set of rules to be followed by the vehicles in case the vehicles have conflicting trajectories in the intersection. In Lu and Kim's paper, 9 instead of discretizing the intersection, the vehicle's actual occupancy is used. The motivation of Lu and Kim 9 is to overcome the increase in computational complexity of the reservation-based scheduling when the granularity of cells is high.…”

Section: Related Workmentioning

confidence: 99%

“…In Lu and Kim's paper, 9 instead of discretizing the intersection, the vehicle's actual occupancy is used. The motivation of Lu and Kim 9 is to overcome the increase in computational complexity of the reservation-based scheduling when the granularity of cells is high. Hence, instead of discretizing the intersection, the discrete time sequence of the vehicles' actual occupancy in the intersection is obtained and used to detect conflicting trajectories.…”

Section: Related Workmentioning

confidence: 99%

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Reservation-based traffic management for autonomous intersection crossing

Choi

Rubenecia

Choi

2019

International Journal of Distributed Sensor Networks

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A scheduling scheme for autonomous intersection crossing is proposed and evaluated. The scheduling scheme determines the flow of autonomous vehicles into an intersection without traffic signals. The objective of the scheduling scheme is to schedule the vehicles’ entrances into the intersection such that there will be no collision among vehicles and the intersection is efficiently utilized. Our scheduling scheme uses reservation-based scheduling approach, and the scheduling is formulated as an optimization problem to find the best sequence of vehicles’ entrances into the intersection. Our scheme is made more practical by allowing the vehicles to move at any speed within a speed range, and it is shown that it is fast enough to be used in real time. Through simulation, it is also shown that our scheduling scheme significantly outperforms the first-come, first-served approach, which is the approach used by other reservation-based scheduling schemes.

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“…With the increasing penetration of PV, the large deviations of the PV output power result in a series problem such as voltage deviation, frequency oscillation, even unplanned islanding. Based on this [30]- [34], the proposed CNN based image regression model is focusing on building a relationship between the input cloudy sky images and the solar irradiations. Recently, the CNN based image processing, especially for image classification, is developing very fast.…”

Section: Introductionmentioning

confidence: 99%

Solar Irradiance Capturing in Cloudy Sky Days–A Convolutional Neural Network Based Image Regression Approach

Jiang

Xie

et al. 2020

IEEE Access

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Global horizontal irradiance (GHI) is a critical index to indicate the output power of the photovaltaic (PV). In traditional approaches, the local GHI can be measured with very expensive instruments, and the large-area GHI collection depends on complex satellite-based models, solargis algorithms, and the high-performance computers (HPC). In this paper, a novel approach is proposed to capture the GHI conveniently and accurately. Considering the nonstationary property of the GHI on cloudy days, the GHI capturing is cast as an image regression problem. In traditional approaches, the image regression problem is treated as two parts, feature extraction (for the images) and regression model (for the regression targets), which are optimized separately and blocked the interconnections. Considering the nonlinear regression capability, a convolutional neural network (CNN) based image regression approach is proposed to provide an End-to-End solution for the cloudy day GHI capturing problem in this paper. The multilayer CNN is based on the AlexNet and VGG. The L2 (least square errors) with regularization is used as the loss function in the regression layer. For data cleaning, the Gaussian mixture model with Bayesian inference is employed to detect and eliminate the anomaly data in a nonparametric manner. The purified data are used as input data for the proposed image regression approach. In the experiments, three-month sky images and GHI data (with 1-min resolution) are provided by the National Renewable Energy Laboratory (NREL) with the HPC system. The numerical results demonstrate the feasibility and effectiveness of the proposed approach.INDEX TERMS Convolutional neural network, solar irradiation, global horizontal irradiance, image regression, variational inference, Bayesian theory, Dirichlet process, deep learning, sky image. NOMENCLATURE PARAMETERS AND VARIABLESt sr i 2 : The times of sunrise for day i 2 t ss i 2 : The times of sunset for day i 2 β miss (t 2 ) : The missing GHI data at time t 2 h j 2 , a j 2 : Coefficients of the spline interpolation X : The sample set or the observation set (GHI) Z : The component index set π: The mixing weights µ: The mean of the normal distributionsThe associate editor coordinating the review of this manuscript and approving it for publication was Hui Liu .

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Autonomous and connected intersection crossing traffic management using discrete-time occupancies trajectory

Cited by 22 publications

References 33 publications

Analysis of V2V Messages for Car-Following Behavior with the Traffic Jerk Effect

Analysis of V2V Messages for Car-Following Behavior with the Traffic Jerk Effect

Reservation-based traffic management for autonomous intersection crossing

Solar Irradiance Capturing in Cloudy Sky Days–A Convolutional Neural Network Based Image Regression Approach

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