A Novel Safe Merging Algorithm for Connected Vehicles Using NetLogo

Bozuyla, Mehmet; Tola, Abdullah T.; Murat, Yetiş Şazi

doi:10.5755/j01.eie.24.3.20956

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…These errors are less significant for vehicles at higher speeds but it should be taken into account for relatively slow moving vehicles. The average rate of acceleration of all the ordinary vehicles is between 1 m/s 2 and 4 m/s 2 [13]. Acceleration, considering real models, is varied and depends on a vehicle type and a gear number [14].…”

Section: The Design Of a Traffic Monitoring System And Sources Of mentioning

confidence: 99%

Vehicle Speed and Length Estimation Errors Using the Intelligent Transportation System with a Set of Anisotropic Magneto-Resistive (AMR) Sensors

Markevičius

Navikas

Idźkowski

et al. 2019

Sensors

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Seeking an effective method for estimating the speed and length of a car is still a challenge for engineers and scientists who work on intelligent transportation systems. This paper focuses on a self-developed system equipped with four anisotropic magneto-resistive (AMR) sensors which are placed on a road lane. The piezoelectric polyvinylidene fluoride (PVDF) sensors are also mounted and used as a reference device. The methods applied in the research are well-known: the fixed threshold-based method and the adaptive two-extreme-peak detection method. However, the improved accuracy of estimating the length by using one of the methods, which is based on computing the difference quotient of a time-discrete signal (representing the changes in the magnitude of the magnetic field of the Earth), is observed. The obtained results, i.e., the speed and length of a vehicle, are presented for various values of the increment Δn used in numerical differentiation of magnetic field magnitude data. The results were achieved in real traffic conditions after analyzing a data set M = 290 of vehicle signatures. The accuracy was evaluated by calculating MAE (Mean Absolute Error), RMSE (Root Mean Squared Error) for different classes of vehicles. The MAE is within the range of 0.52 m–1.18 m when using the appropriate calibration factor. The results are dependent on the distance between sensors, the speed of vehicle and the signal processing method applied.

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Section: The Design Of a Traffic Monitoring System And Sources Of mentioning

confidence: 99%