scholarly journals A Review of Automatic Driving System by Recognizing Road Signs Using Digital Image Processing

2021 ◽  
Vol 2 (2) ◽  
pp. 1-9
Author(s):  
Afroj Alam ◽  
Keyword(s):  
Review Paper ◽  
Item Recognition ◽  
High Quality ◽  
Driving System ◽  
Survey Paper ◽  
Automatic Driving ◽  
Road Signs ◽  
Optical Flow Method ◽  
Short Presentation ◽  
Item Location

In this review, the paper furnishes object identification's relationship with video investigation and picture understanding, it has pulled in much exploration consideration as of late. Customary item identification strategies are based on high-quality highlights and shallow teachable models. This survey paper presents one such strategy which is named as Optical Flow method. This strategy is discovered to be stronger and more effective for moving item recognition and the equivalent has been appeared by an investigation in this review paper. Applying optical stream to a picture gives stream vectors of the focus-es comparing to the moving items. Next piece of denoting the necessary moving object of interest checks to the post preparation. Post handling is the real commitment of the review paper for moving item identification issues. Their presentation effectively deteriorates by developing complex troupes which join numerous low-level picture highlights with significant level setting from object indicators and scene classifiers. With the fast advancement in profound learning, all the more useful assets, which can learn semantic, significant level, further highlights, are acquainted with address the issues existing in customary designs. These models carry on contrastingly in network design, preparing system, and advancement work, and so on In this review paper, we give an audit on pro-found learning-based item location systems. Our survey starts with a short presentation on the historical backdrop of profound learning and its agent device, in particular Convolutional Neural Network (CNN).

scholarly journals A Review of Object Visual Detection for Intelligent Vehicles

2021 ◽  
pp. 1-10
Author(s):  
Sirin Kumar Singh ◽  
Keyword(s):  
Neural Network ◽  
Review Paper ◽  
Visual Detection ◽  
Item Recognition ◽  
Intelligent Vehicles ◽  
High Quality ◽  
Survey Paper ◽  
Optical Flow Method ◽  
Short Presentation ◽  
Item Location

This paper contains the details of different object detection (OD) techniques, object identification's relationship with video investigation, and picture understanding, it has pulled in much exploration consideration as of late. Customary item identification strategies are based on high-quality highlights and shallow teachable models. This survey paper presents one such strategy which is named as Optical Flow method (OFM). This strategy is discovered to be stronger and more effective for moving item recognition and the equivalent has been appeared by an investigation in this review paper. Applying optical stream to a picture gives stream vectors of the focuses comparing to the moving items. Next piece of denoting the necessary moving object of interest checks to the post-preparing. Post handling is the real commitment of the review paper for moving item identification issues. Their presentation effectively deteriorates by developing complex troupes which join numerous low-level picture highlights with significant level set-ting from object indicators and scene classifiers. With the fast advancement in profound learning, all the more useful assets, which can learn semantic, significant level, further highlights, are acquainted with address the issues existing in customary designs. These models carry on contrastingly in network design, preparing system, and advancement work, and so on in this review paper, we give an audit on profound learning-based item location systems. Our survey starts with a short presentation on the historical backdrop of profound learning and its agent device, in particular, Convolutional Neural Network (CNN) and region-based convolutional neural networks (R-CNN).

Level-wise driving knowledge induction for embedded automatic train driving system

2021 ◽  
pp. 107754632110033
Author(s):  
Gang Xiao ◽  
Qinwen Yang ◽  
Fan Yang ◽  
Tao Liu ◽  
Tao Li ◽  
...  
Keyword(s):  
Embedded System ◽  
Fuzzy Rule ◽  
Operating Efficiency ◽  
Driving Experience ◽  
Driving System ◽  
Automatic Driving ◽  
Energy Consumption Reduction ◽  
Experience Levels ◽  
Consumption Reduction ◽  
Regression Learning

Automatic driving of trains can significantly reduce the energy cost and enhance the operating efficiency and safety. The automatic train driving system has to be an embedded system that can run onboard with low power, which necessitates an efficient inference model. In this article, a level-wise driving knowledge induction approach is proposed for embedded automatic train driving systems. The coincident driving patterns in the records of drivers with different experience levels suggest the suitability of a driving experience knowledge rule induction approach. We design a two-level learning approach to obtain both the driving experience pattern in fuzzy rule-based knowledge form and the detailed parameters of velocity and gear by regression learning methods. With 8.93% energy consumption reduction compared with average human drivers, the experiments indicate the effectiveness of our approach.

scholarly journals McStas (ii): An overview of components, their use, and advice for user contributions

2021 ◽  
Vol 23 (1) ◽  
pp. 7-27
Author(s):  
Peter Kjær Willendrup ◽  
Kim Lefmann
Keyword(s):  
Review Paper ◽  
Time Of Flight ◽  
Physical Models ◽  
High Quality ◽  
Physical Elements ◽  
Main Component ◽  
Continuous Source ◽  
Component Class

A key element of the success of McStas is the component layer where users and developers alike are contributing to the description of new physical models and features. In McStas, components realise all physical elements of the simulated instrument from source via optics and samples to detector. In this second review paper of the McStas package, we present an overview of the component classes in McStas: sources, monitors, optics, samples, misc, and contrib. Within each component class we give thorough examples of high-quality components, including their algorithms and example use. We present two example instruments, one for a continuous source and one for a time-of-flight source, that together demonstrate the use of the main component classes. Finally, we give tips and instructions that will allow the reader to write good components and elucidate the pathway of contributing new components to McStas.

scholarly journals Research on Automatic Train Driving Based on Fuzzy Proportion Integration Differentiation Iterative Control

2021 ◽  
Vol 283 ◽  
pp. 02021
Author(s):  
Zhengsheng Qi ◽  
Bohong Liu ◽  
Mengmeng Wang
Keyword(s):  
High Speed ◽  
Convergence Condition ◽  
Vital Role ◽  
Fuzzy Pid ◽  
Parameter Uncertainties ◽  
Efficient Operation ◽  
Transportation Services ◽  
Driving System ◽  
Automatic Driving ◽  
Iterative Control

Automatic train driving system is an important subsystem of train operation control system, which can provide passengers with punctual, accurate, efficient and fast transportation services. At the same time, the accurate stop, comfort and stability of the train is an important index to measure the control performance of the train automatic driving system, and the accurate stop plays a vital role in the efficient operation of the train. Based on the characteristics of high-speed train parking, an accurate parking algorithm based on fuzzy PID iterative control was proposed to solve the problem of low parking accuracy caused by frequent switching of control output. On the basis of solving the differential equation of the train braking model, the gradient of the system is obtained, and then the learning parameters of the convergence condition are obtained to overcome the repeated uncertainty in the stopping stage. The simulation results show that the fuzzy PID iterative control for asymptotic stability is an effective method to realize the precise parking of trains, and has strong robustness against the train parameter uncertainties and external disturbances.

scholarly journals Influences of Single-Lane Automatic Driving Systems on Traffic Efficiency and CO2 Emissions on China’s Motorways

2021 ◽  
Vol 11 (22) ◽  
pp. 11032
Author(s):  
Haokun Song ◽  
Fuquan Zhao ◽  
Zongwei Liu
Keyword(s):  
Co2 Emissions ◽  
Penetration Rate ◽  
Connected Vehicles ◽  
Driving System ◽  
Traffic Efficiency ◽  
Automatic Driving ◽  
Driving Behaviors ◽  
Road Capacity ◽  
Long Time ◽  
Carbon Dioxide Co2

There are big differences between the driving behaviors of intelligent connected vehicles (ICVs) and traditional human-driven vehicles (HVs). ICVs will be mixed with HVs on roads for a long time in the future. Different intelligent functions and different driving styles will affect the condition of traffic flow, thereby changing traffic efficiency and emissions. In this paper, we focus on China’s expressways and secondary motorways, and the impacts of the ‘single-lane automatic driving system’ (SLADS) on traffic delay, road capacity and carbon dioxide (CO2) emissions were studied under different ICV penetration rates. Driving styles were regarded as important factors for scenario analysis. We found that with higher volume input, SLADS has an optimizing effect on traffic efficiency and CO2 emissions generally, which will be more significant as the ICV penetration rate increases. Additionally, enhancing the aggressiveness of driving behavior appropriately is an effective way to amplify the benefits of SLADS.

Automatic Driving System Based on Embedded Microcontroller Using Human Skill Models

2011 ◽  
Vol 6 (2) ◽  
Author(s):  
Shangchang Ma ◽  
Bifeng Yang ◽  
Yanjun Zhan ◽  
Sujuan Zhang ◽  
Chao Wang
Keyword(s):  
Driving System ◽  
Automatic Driving ◽  
Human Skill

Modular Neural Network for Learning Visual Features, Routes, and Operation Through Human Driving Data Toward Automatic Driving System

2020 ◽  
Vol 24 (3) ◽  
pp. 368-376
Author(s):  
Shun Otsubo ◽  
Yasutake Takahashi ◽  
Masaki Haruna ◽  
◽  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Visual Information ◽  
Machine Learning Techniques ◽  
Driving System ◽  
Modular Neural Networks ◽  
Automatic Driving ◽  
Learning Module ◽  
Learning Techniques ◽  
Almost All

This paper proposes an automatic driving system based on a combination of modular neural networks processing human driving data. Research on automatic driving vehicles has been actively conducted in recent years. Machine learning techniques are often utilized to realize an automatic driving system capable of imitating human driving operations. Almost all of them adopt a large monolithic learning module, as typified by deep learning. However, it is inefficient to use a monolithic deep learning module to learn human driving operations (accelerating, braking, and steering) using the visual information obtained from a human driving a vehicle. We propose combining a series of modular neural networks that independently learn visual feature quantities, routes, and driving maneuvers from human driving data, thereby imitating human driving operations and efficiently learning a plurality of routes. This paper demonstrates the effectiveness of the proposed method through experiments using a small vehicle.

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