moving vehicles
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Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 265
Author(s):  
Sotirios Kontogiannis ◽  
Anestis Kastellos ◽  
George Kokkonis ◽  
Theodosios Gkamas ◽  
Christos Pikridas

Accidents in highway tunnels involving trucks carrying flammable cargoes can be dangerous, needing immediate confrontation to detect and safely evacuate the trapped people to lead them to the safety exits. Unfortunately, existing sensing technologies fail to detect and track trapped persons or moving vehicles inside tunnels in such an environment. This paper presents a distributed Bluetooth system architecture that uses detection equipment following a MIMO approach. The proposed equipment uses two long-range Bluetooth and one BLE transponder to locate vehicles and trapped people in motorway tunnels. Moreover, the detector’s parts and distributed architecture are analytically described, along with interfacing with the authors’ resources management system implementation. Furthermore, the authors also propose a speed detection process, based on classifier training, using RSSI input and speed calculations from the tunnel inductive loops as output, instead of the Friis equation with Kalman filtering steps. The proposed detector was experimentally placed at the Votonosi tunnel of the EGNATIA motorway in Greece, and its detection functionality was validated. Finally, the detector classification process accuracy is evaluated using feedback from the existing tunnel inductive loop detectors. According to the evaluation process, classifiers based on decision trees or random forests achieve the highest accuracy.


Author(s):  
Zengfang Shi ◽  
Meizhou Liu

The existing target detection and recognition technology has the problem of fuzzy features of moving vehicles, which leads to poor detection effect. A moving car detection and recognition technology based on artificial intelligence is designed. The point operation is adopted to enhance the high frequency information of the image, increase the image contrast, and delineate the video image tracking target. The motion vector similarity is used to predict the moving target area in the next frame of the image. The texture features of the moving car are extracted by artificial intelligence, and the center moment is calculated by the gray histogram distribution curve, the edge feature extraction algorithm is used to set the detection and recognition mode. Experimental results: under complex conditions, this design technology, compared with the other two kinds of moving vehicle detection and recognition technology, detected three more moving vehicles, which proved that the application prospect of the moving vehicle detection and recognition technology integrated with artificial intelligence is broader.


2022 ◽  
pp. 161-219
Author(s):  
Chi-Hsuan Huang ◽  
Yu Sun ◽  
Chiou-Shana Fuh

In this chapter, an AI (artificial intelligence) solution for LPR (license plate recognition) on moving vehicles is proposed. The license plates in images captured with cameras on moving vehicles have unpredictable distortion and various illumination which make traditional machine vision algorithms unable to recognize the numbers correctly. Therefore, deep learning is leveraged to recognize license plate in such challenging conditions for better recognition accuracy. Additionally, lightweight neural networks are chosen since the power supply of scooter is quite limited. A two-stage method is presented to recognize license plate. First, the license plates in captured images are detected using CNN (convolutional neural network) model and the rotation of the detected license plates are corrected. Subsequently, the characters are recognized as upper-case format (A-Z) and digits (0-9) with second CNN model. Experimental results show that the system achieves 95.7% precision and 95% recall at high speed during the daytime.


2022 ◽  
Vol 1212 (1) ◽  
pp. 012044
Author(s):  
Y Sari ◽  
P B Prakoso ◽  
A R Baskara

Abstract Detecting moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detecting moving vehicles is also part of the detection of moving objects. K-Means method has been successfully applied to unsupervised cluster pixels for the detection of moving objects. In general, K-Means is a heuristic algorithm that partitioned the data set into K clusters by minimizing the number of squared distances in each cluster. In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. The aim of this study is to compare and evaluate the implementation of these distances in the K-Means clustering algorithm. The comparison is done with the basis of K-Means assessed with various evaluation paramaters, namely MSE, PSNR, SSIM and PCQI. The results exhibit that the Manhattan distance delivers the best MSE, PSNR, SSIM and PCQI values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hariprasath Manoharan ◽  
Yuvaraja Teekaraman ◽  
Ramya Kuppusamy ◽  
Arun Radhakrishnan

This article addresses the importance of parking system which makes the movement of moving vehicles to be unrestricted thus providing integration between hominid classification and sensing systems. If two distinct systems are combined, then all the vehicles can monitor the parking space, and they can directly move towards the destination end within short span of time. In addition for this type of establishment, rapidity of transportation vehicles is calculated with error minimization technique where all technical hitches will be avoided by sustaining the user constraints. Further, to solve the designed user constraints, a nonlinear optimization which is termed as machine learning algorithm is introduced for avoiding high loss during packet transmission technique, and percentage of efficiency is analyzed using simulated results with network simulator (NS2). Moreover, from simulated results, it is substantiated that the projected method on automatic parking of vehicles provides high efficient operation, and even cost of installation is reduced.


Author(s):  
H. Echab ◽  
A. Khallouk ◽  
H. Ez-Zahraouy

The objective of this study was to investigate the impact of connected and autonomous vehicles (CAVs) on traffic flow under various parameters. For this purpose, we propose a mixed CAV and conventional vehicle (CV) model to investigate a bidirectional two-lane traffic flow under the periodic boundary condition. The traffic flux and the phase diagrams of the system in the ([Formula: see text]) area are constructed in both cases: with and without CAVs. The overtaking frequency is also calculated. The simulation findings show that the traffic capacity is greatly enhanced with the increase in the CAV penetration ratio. Owing to the cooperative driving strategy, with the increase in penetration ratio of the CAV, the portion of smooth overtaking is boosted. Furthermore, it is found that the traffic throughput is positively correlated to the speed limit of the fast vehicle where the flux increases as [Formula: see text] increases. Also, even if there is a low rate of slow moving vehicles in the system, it will have an appreciable and a significant negative influence.


2021 ◽  
Vol 14 (1) ◽  
pp. 264
Author(s):  
Zhifa Yang ◽  
Yu Zhu ◽  
Haodong Zhang ◽  
Zhuo Yu ◽  
Shiwu Li ◽  
...  

The vehicle detection method plays an important role in the driver assistance system. Therefore, it is very important to improve the real-time performance of the detection algorithm. Nowadays, the most popular method is the scanning method based on sliding window search, which detects the vehicle from the image to be detected. However, the existing sliding window detection algorithm has many drawbacks, such as large calculation amount and poor real-time performance, and it is impossible to detect the target vehicle in real time during the motion process. Therefore, this paper proposes an improved hierarchical sliding window detection algorithm to detect moving vehicles in real time. By extracting the region of interest, the region of interest is layered, the maximum and minimum values of the detection window in each layer are set, the flashing frame generated by the layering is eliminated by the delay processing method, and a method suitable for the motion is obtained: the real-time detection algorithm of the vehicle, that is, the hierarchical sliding window detection algorithm. The experiments show that the more layers are divided, the more time is needed, and when the number of detection layers is greater than 7, the time change rate increases significantly. As the number of layers decreases, the detection accuracy rate also decreases, resulting in the phenomenon of a false positive. Therefore, it is determined to meet the requirements of real time and accuracy when the image is divided into 7 layers. It can be seen from the experiment that when the images to be detected are divided into 7 layers and the maximum and minimum values of detection windows are 30 × 30 and 250 × 250, respectively, the number of sub-windows generated is one thirty-seventh of the original sliding window detection algorithm, and the execution time is only one-third of the original sliding window detection algorithm. This shows that the hierarchical sliding window detection algorithm has better real-time performance than the original sliding window detection algorithm.


2021 ◽  
Vol 8 (2) ◽  
pp. 8-14
Author(s):  
Julkar Nine ◽  
Aarti Kishor Anapunje

Vehicle detection is one of the primal challenges of modern driver-assistance systems owing to the numerous factors, for instance, complicated surroundings, diverse types of vehicles with varied appearance and magnitude, low-resolution videos, fast-moving vehicles. It is utilized for multitudinous applications including traffic surveillance and collision prevention. This paper suggests a Vehicle Detection algorithm developed on Image Processing and Machine Learning. The presented algorithm is predicated on a Support Vector Machine(SVM) Classifier which employs feature vectors extracted via Histogram of Gradients(HOG) approach conducted on a semi-real time basis. A comparison study is presented stating the performance metrics of the algorithm on different datasets.


2021 ◽  
Vol 13 (24) ◽  
pp. 14073
Author(s):  
Władysław Hamiga ◽  
Wojciech Ciesielka

In recent years there has been dynamic progress in the development of fully autonomous trucks and their combination and coordination into sets of vehicles moving behind each other within short distances, i.e., platooning. Numerous reports from around the world present significant benefits of platooning for the environment due to reduced emissions, reduced fuel costs, and improved logistics in the transport industry. This paper presents original aerodynamic and aeroacoustic studies of identical truck column models. They are divided into four main stages. In the first, a truck model and three columns of identical trucks with different distances between the vehicles was made and tested using computational fluid dynamics (CFD). Two turbulence models were used in the study: k−ω shear stress transport (SST) and large eddy simulation (LES). The aim of the work was to determine the drag coefficients for each set of vehicles. The second stage of work included determination of sound field distributions generated by moving vehicles. Using the Ffowcs Williams–Hawkings (FW-H) analogy, the sound pressure levels were determined, followed by the sound pressure levels A. In order to verify the correctness of the work carried out, field tests were also performed and additional acoustic calculations were carried out using the NMPB-Routes-2008 and ISO 9613-2 models. Calculations were performed using SoundPlan software. The performed tests showed good quality of the built aerodynamic and aeroacoustic models. The results presented in this paper have a universal character and can be used to build intelligent transport systems (ITSs) and intelligent environmental management systems (IEMSs) for municipalities, counties, cities, and urban agglomerations by taking into account the platooning process.


2021 ◽  
Vol 6 (2) ◽  
pp. 105-111
Author(s):  
Yevhen Fastiuk ◽  
◽  
Ruslan Bachynskyy ◽  
Nataliia Huzynets

In this era, people using vehicles is getting increased day by day. As pedestrians leading a dog for a walk, or hurrying to their workplace in the morning, we’ve all experienced unsafe, fast-moving vehicles operated by inattentive drivers that nearly mow us down. Many of us live in apartment complexes or housing neighborhoods where ignorant drivers disregard safety and zoom by, going way too fast. To plan, monitor and also control these vehicles is becoming a big challenge. In the article, we have come up with a solution to the above problem using the video surveillance considering the video data from the traffic cameras. Using computer vision and deep learning technology we will be able to recognize violations of rules. This article will describe modern CV and DL methods to recognize vehicle on the road and traffic violations of rules by them. Implementation of methods can be done using OpenCV Python as a tool. Our proposed solution can recognize vehicles, track their speed and help in counting the objects precisely.


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