Використання нейронної мережі для розроблення системи уникнення перешкод на дорозі

2020 ◽  
pp. 24-33
Author(s):  
Уляна Дзелендзяк ◽  
◽  
Мішель Вигриновський ◽  
Keyword(s):  
Neural Network ◽  
The Road ◽  
The Neural Network ◽  
On The Road

The possibility of using a neural network to implement a system of avoidance of obstacles on the road has been investigated. The algorithms based on which such a system can work has been reviewed, also the principle of learning of the neural network has been considered. In order to implement investigation the simulator based on Unity and ML Agents has been developed. Using simulator the efficiency of education and this neural network in different configurations has been investigated.

Vehicle License Plate Registration Recognition System

2013 ◽  
Vol 718-720 ◽  
pp. 2286-2290
Author(s):  
Embiale Merkebu Tiruneh ◽  
De Ning Jiang
Keyword(s):  
Neural Network ◽  
Rapid Growth ◽  
Recognition System ◽  
License Plate ◽  
The Road ◽  
The Neural Network ◽  
Vehicle Registration ◽  
On The Road ◽  
Improved Technology ◽  
Analyze Data

Neural network had been used widely in many applications, such as to recognize an object or character, to detect a motion, to control a process, to forecast a result, to analyze data and for management of information. With the rapid growth of vehicles on the road and with the aid of improved technology, there is a demand for processing vehicles as conceptual resources in information systems. This paper will show how to design a system using the neural network to recognize the vehicle registration plate of vehicles. The approach to the project is by capturing footage and after which, the footage undergo segmentation to obtain the vehicle registration plate numbers using this software called MATLAB. The simulation will be illustrated after training the neural network; the system is able to recognize most of the vehicle registration plate with minimum errors.

scholarly journals An Automatic Traffic Rules Violation Detection and Number Plate Recognition System for Bangladesh

2020 ◽  
Vol 19 (2) ◽  
pp. 87-98
Author(s):  
Raian Shahrear ◽  
Md. Anisur Rahman ◽  
Atif Islam ◽  
Chamak Dey ◽  
Md. Saniat Rahman Zishan
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
The Road ◽  
Controlling System ◽  
Detection And Identification ◽  
Violation Detection ◽  
Computer Vision Technology ◽  
On The Road ◽  
Day By Day ◽  
Traffic Rules

The traffic controlling system in Bangladesh has not been updated enough with respect to fast improving technology. As a result, traffic rules violation detection and identification of the vehicle has become more difficult as the number of vehicles is increasing day by day. Moreover, controlling traffic is still manual. To solve this problem, the traffic controlling system can be digitalized by a system that consists of two major parts which are traffic rules violation detection and number plate recognition. In this research, these processes are done automatically which is based on machine learning, deep learning, and computer vision technology. Before starting this process, an object on the road is identified through the YOLOv3 algorithm. By using the OpenCV algorithm, traffic rules violation is detected and the vehicle that violated these rules is identified. To recognize the number plate of the vehicle, image acquisition, edge detection, segmentation of characters is done sequentially by using Convolution Neural Network (CNN) in MATLAB background. Among the traffic rules, the following traffic signal is implemented in this research.

Use of Neural Network and Discrete Wavelet Transformations in Estimation of Road Profile

Volume 2 ◽  
2004 ◽  
Author(s):  
Mohammad Durali ◽  
Alireza Kasaaizadeh
Keyword(s):  
Neural Network ◽  
Optimization Methods ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network ◽  
The Road ◽  
The Neural Network ◽  
Road Profile ◽  
Road Roughness ◽  
Coding Method ◽  
Dynamic Experiments

This paper presents a method for estimation of road profile for automotive research applications with more accuracy and higher speed. Dynamic response of a car equipped with position and velocity sensors and driving on a sample road is used as basic data. A feed-forward neural network, trained with outputs from a car model in ADAMS, is used as the car inverse model. The neural network is capable of estimating the road roughness from the car response during test drives. The ADAMS model is corrected and validated using a series of dynamic experiments on the car, performed on a hydro-pulse test rig. The only problem in this approach, like other identification and optimization methods, is the large volume of generated data in time domain, acquired from car response during road test. This problem is solved using wavelet methods to code the acquired data. Unlike all frequency methods that eliminate a large portion of the data details during processing, the wavelet coding method restores most of the details, while the volume of the stored data is kept to a minimum. The results show that this method can estimate the road profile accurately and with great savings in processing time and effort.

scholarly journals Research on Road Roughness Based on NARX Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yingjie Liu ◽  
Dawei Cui
Keyword(s):  
Neural Network ◽  
The Body ◽  
Identification Accuracy ◽  
Vertical Acceleration ◽  
Plane Model ◽  
The Road ◽  
The Neural Network ◽  
Road Roughness ◽  
Network Identification ◽  
Narx Neural Network

In order to solve the problem of road roughness identification, a study on the nonlinear autoregressive with exogenous inputs (NARX) neural network identification method was carried out in the paper. Firstly, a 7-DOF plane model of vehicle vibration system was established to obtain the vertical acceleration and elevation acceleration of the body, which were set as ideal input samples for the neural network. Then, based on the plane model, with common speed, the road roughness was solved as the ideal output sample of the NARX neural network, and the road roughness of B-level and C-level was identified. The results show that the proposed method has ideal identification accuracy and strong antinoise ability. The relative error of C-level road roughness is larger than that of B-level road roughness. The identified road roughness can provide a theoretical basis for analyzing the dynamic response of expressway roads.

Study of Highway Crack Diagnosis Based on Cellular Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 2013-2017
Author(s):  
Sheng Zhuo Yao ◽  
Guo Dong Li ◽  
Fu Xin Zhang ◽  
Lin Ge
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Cellular Neural Network ◽  
Cellular Neural Networks ◽  
Quality Information ◽  
Image Detection ◽  
The Road ◽  
Standard Level ◽  
On The Road

Road quality information detect system is an important component in architecture quality detect system, also is the basement of successfully working of other related project for the whole country. The study of detecting the road crack is the key to insure the security of accurately detect the road quality in transportation system. In this paper, we come up with a fixed way of road undersized rift image detection by using cellular neural networks. By image processing, building rift networks and details networks and adding the model of similarity undersized rift networks. It can avoid the problem that can not accurately detect undersized crack by only taking the crack feature value. The experiment proved that fixed crack detect computing is easy to do, more accurate to detect the undersized cracks on the road and can reach the standard level of current detect technique.

scholarly journals Dynamic Estimation of Saturation Flow Rate at Information-Rich Signalized Intersections

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 178 ◽  
Author(s):  
Yi Wang ◽  
Jian Rong ◽  
Chenjing Zhou ◽  
Yacong Gao
Keyword(s):  
Neural Network ◽  
Flow Rate ◽  
Network Models ◽  
Signalized Intersections ◽  
Neural Network Models ◽  
The Road ◽  
The Neural Network ◽  
Dynamic Estimation ◽  
Saturation Flow Rate ◽  
Saturation Flow

Intersections are the bottlenecks of the road network. The capacity of signalized intersections restricts the operation of the road network. Dynamic estimation of capacity is necessary for signalized intersections refined management. With the development of technology, more and more detectors were installed near the intersection. It had been the information-rich environment, which provided support for dynamic estimation of capacity. A dynamic estimation method for a saturation flow rate based on a neural network was developed. It would grasp the dynamic change of saturation flow rates and influencing factors. The measure data at three scenarios (through lanes, shared right-turn and through lanes, shared left-turn and through lanes) of signalized intersections in Beijing were taken as examples to validate the proposed method. Firstly, the traffic flow characteristics of the three scenarios and factors affecting the saturation flow rate were analyzed. Secondly, neural network models of the three scenarios were established. Then the hyperparameters of neural network models were determined. After training, the neural network structure and parameters were saved. Lastly, the test set data was validated by the training model. At the same time, the proposed method was compared with the Highway Capacity Manual (HCM) method and the statistical regression method. The results show that both regression models and neural network models have better accuracy than HCM models. In a simple scenario, the neural network models are not much different from the regression models. With the increase of complexity of scenarios, the advantages of neural network models are highlighted. In through-left lane and through-right lane scenarios, the estimated saturation flow rates used by the proposed method were 7.02%, 4.70%, respectively. In the complexity of traffic scenarios, the proposed method can estimate the saturation flow rate accurately and timely. The results could be used for signal control schemes optimizing and operation managing at signalized intersections subtly.

scholarly journals OBJECT RECOGNITION OF ROAD INFRASTRUCTURE USING A FULLY CONVOLUTIONAL NEURAL NETWORK

2019 ◽  
Vol 2019 (4) ◽  
pp. 38-43
Author(s):  
Andrey Azarchenkov ◽  
Maxim Lyubimov ◽  
Vladislav Lushkov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Urban Infrastructure ◽  
Road Infrastructure ◽  
Traffic Lights ◽  
The Road ◽  
Segmented Image ◽  
The Neural Network ◽  
Wheeled Robot ◽  
Pedestrian Crossing

This article presents a method for recognizing key objects of the road infrastructure using a fully convolutional neural network. The result of the neural network is a segmented image, where the desired objects are highlighted in certain colors. At the post-processing stage, a section of the roadway along which the car moves is selected, as well as the calculation of the parameters of the bounding rectangles for each of the objects. This method allows you to localize the road, pedestrian crossing, cars, traffic signs, traffic lights, pedestrians. Testing of the developed algorithm was carried out on a model of the urban infrastructure at a scale of 1:18, where a wheeled robot acted as a car.

scholarly journals Use of Machine Learning in Automobile Industry to Improve Safety Using CNN

2021 ◽  
Vol 9 (10) ◽  
pp. 1807-1810
Author(s):  
Samkit Saraf
Keyword(s):  
Automobile Industry ◽  
Steering System ◽  
Signal Recognition ◽  
The Road ◽  
Road Signs ◽  
The Neural Network ◽  
Traffic Recognition ◽  
On The Road ◽  
Positive Results

Abstract: Vision-based vehicle steering system cars can have three main roles: 1) road access; 2) an obstacle to find; and 3) signal recognition. The first two have already been taught many years and there have been many positive results, but a sign of traffic recognition is a less readable field. Road signs provide drivers with the most important information on the road, to do driving is safe and easy. We think road signs should play the same role of private cars. The color and shape are very different from the natural environment. The algorithm described in this paper uses this feature. It has two main parts. The first, to find, uses color range to separate image analysis and shapes to get symptoms. The second, in stages, uses the neural network. Some effects from natural forums are shown. On the other hand, the algorithm works to detect other types of marks can tell a moving robot to perform a specific task that place. Keywords: o Traffic signs o CNN o Cars o Image processing o Classification

scholarly journals The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fan Hou ◽  
Yue Zhang ◽  
Xinli Fu ◽  
Lele Jiao ◽  
Wen Zheng
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Road Network ◽  
Short Term Memory ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Point Of Interest ◽  
Flow Prediction ◽  
Long Short Term Memory ◽  
On The Road

Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). The model can capture the complex spatial dependence of road nodes on the road network and use LSGC (local spectrogram convolution) to capture spatial correlation features from the K-order local neighbors of the road segment nodes in the road network. It is more accurate to extract the information of neighbor nodes by replacing the single-hop neighborhood matrix with K-order local neighborhoods to expand the receptive field of graph convolution. The high-order neighborhood of road nodes is also fully considered instead of only extracting features from first-order neighbor nodes. In addition, an external attribute enhancement unit is designed to extract external factors (weather, point of interest, time, etc.) that affect traffic flow in order to improve the accuracy of the model’s traffic flow prediction. The experimental results show that when considering the static, dynamic, and static and dynamic combination, the model has excellent performance: RMSE (4.0406, 4.0362, 4.0234), MAE (2.7184, 2.7044, 2.7030), accuracy (0.7132, 0.7190, 0.7223).

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