Automobile Technology Research Articles

KLASIFIKASI RAMBU LALU LINTAS MENGGUNAKAN DECISION TREE J48 DAN LOCAL BINARY PATTERN

Automatic steering technology or autopilot in cars is developing rapidly. This feature makes it easier for the driver because the car can run according to the program directions. The driver of course can still take control of the car manually as desired, so it is possible for the driver to violate traffic signs, whether intentionally or not. This study seeks to create a traffic sign recognition system that can help reduce violations committed by drivers knowingly. The test is carried out using a combination of the Local Binary Pattern algorithm as feature extraction and Decision Tree J48 algorithm as a classification system to recognize traffic signs. It is hoped that this research can provide an overview of the combined ability of these two algorithms in classifying types of traffic signs.

Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

With the emergence of networked devices, from the Internet of Things (IoT) nodes and cellular phones to vehicles connected to the Internet, there has been an ever-growing expansion of attack surfaces in the Internet of Vehicles (IoV). In the past decade, there has been a rapid growth in the automotive industry as network-enabled and electronic devices are now integral parts of vehicular ecosystems. These include the development of automobile technologies, namely, Connected and Autonomous Vehicles (CAV) and electric vehicles. Attacks on IoV may lead to malfunctioning of Electronic Control Unit (ECU), brakes, control steering issues, and door lock issues that can be fatal in CAV. To mitigate these risks, there is need for a lightweight model to identify attacks on vehicular systems. In this article, an efficient model of an Intrusion Detection System (IDS) is developed to detect anomalies in the vehicular system. The dataset used in this study is an In-Vehicle Network (IVN) communication protocol, i.e., Control Area Network (CAN) dataset generated in a real-time environment. The model classifies different types of attacks on vehicles into reconnaissance, Denial of Service (DoS), and fuzzing attacks. Experimentation with performance metrics of accuracy, precision, recall, and F-1 score are compared across a variety of classification models. The results demonstrate that the proposed model outperforms other classification models.

Automobile Technology and Tradeoffs How Does Automobile Footprint Affect its Fuel Economy?

Automobile Technology and Tradeoffs How Does Automobile Footprint Affect its Fuel Economy?

High‐Voltage Topological Architecture‐Based Energy Management Strategy of the Plug‐In Hybrid Powertrain System

Hybrid technology (including plug‐in hybrid) integrates the advantages of traditional automobile technology and pure electric technology, which can greatly reduce fuel consumption and improve emissions. It has become one of the main technologies developed at present and in the next 15∼20 years. Energy management is the core algorithm of hybrid electric vehicle control strategy, and it is the focus of current research. However, these studies mainly focus on the high efficiency of control assembly, optimal management of power system energy, and maximum recovery of renewable energy but have not considered energy distribution management and optimization between the power battery and the low‐voltage battery. Hence, based on the high‐voltage topology of the plug‐in hybrid system, this paper proposes the optimal energy management strategy between the power battery and the low‐voltage battery under different working conditions. The charging and discharging characteristics of the power battery under different electric quantities are also combined. The experimental results show that based on the optimized energy management strategy, the pure electric driving range is increased by 6% under NEDC condition for a C‐class plug‐in hybrid car, and the energy‐saving effect of the vehicle is further improved.

Design and implementation of intelligent monitoring system for public transport applications

With the rapid development of automobile technologies and great economic growth, cars have become more and more popular for their convenience. However, the increase in cars has caused serious air pollution and increased the probability of traffic accidents. This paper investigates the design and implementation of intelligent monitoring and early warning system, which can detect the traffic conditions and provide environmental monitoring data. To achieve a comprehensive monitor for both inside and outside the public transport, this system is based on the STC12C5A16S2 micro-controller and loaded with all kinds of sensors, such as alcohol sensors, temperature and humidity sensors, toxic gas sensors, photosensitive sensors and light sensors etc. If the system detects any abnormal situation, it will generate early warning signs in time to inform the driver and the traffic regulatory departments. Consequently, our intelligent monitoring system can protect the safety of passengers and reduce the incidence of traffic accidents.

Multi-attribute decision-making method for prioritizing autonomous vehicles in real-time traffic management: towards active sustainable transport

In order to alleviate traffic congestion, traffic control systems are an important tool. These systems strive to boost the efficiency of road systems to optimize traffic flow on individual road segments. The benefits of real-time traffic control systems might be increased by integrating new telecommunication and autonomous car technology. There is six real-time traffic advancing sustainable development examined in this study: variable message signs and ramp meters, traffic diversion, and the integration of driverless vehicles into other traffic management systems, with four key criteria: economics, community and social, ecologic and traffic security, as well as 13 sub-criteria using MCDM. To do this, we offer unique additions to the WASPAS technique.

Deep Learning Control for Autonomous Robot

Several applications of machine learning and artificial intelligence, have acquired importance and come to the fore as a result of recent advances and improvements in these approaches. Autonomous cars are one such application. This is expected to have a significant and revolutionary influence on society. Integration with smart cities, new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving automobiles. The autonomous automobile, often known as self-driving systems or driverless vehicles, is a vehicle that can perceive its surroundings and navigate predetermined routes without human involvement. Cars are on the verge of evolving into autonomous robots, thanks to significant breakthroughs in artificial intelligence and related technologies, and this will have a wide range of socio-economic implications. However, in order for these automobiles to become a reality, they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate action. The majority of self-driving car technologies are based on computer systems that automate vehicle control parts. From forward-collision warning and antilock brakes to lane-keeping and adaptive drive control, to fully automated driving, these technological components have a wide range of capabilities. A self-driving car combines a wide range of sensors, actuators, and cameras. Recent researches on computer vision and deep learning are used to control autonomous driving systems. For self-driving automobiles, lane-keeping is crucial. This study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the lane. We propose an advanced control for a self-driving robot by using two controllers simultaneously. Convolutional neural networks (CNNs) are employed, to predict the car’ and a proportional-integral-derivative (PID) controller is designed for speed and steering control. This study uses a Raspberry PI based camera to control the robot car.

Validity and Reliability of Green Competencies Instrument for Automobile Technology Programme Using Rasch Model

This article aims to determine the validity of developed constructs and check the reliability of the newly developed instrument named as Questionnaire on Green Competencies for Automobile Engineering Technology (QGCAET) for the Automobile Technology Programme in Nigerian Universities. The instrument consists of 170 elements measuring four constructs namely Technical Green Competencies; Managerial Green Competencies; Personal Green Competencies and Social Green Competencies and was administered to 299 respondents made of Lecturers, Technologists and Final- Year Students of Automobile Engineering and Technology programme in Nigeria universities. The Rasch model was used to examine the validity and reliability of the items. From the analysis point of view, the polarity of the elements indicates that the correlation of the point measure (PTMEA CORR) of 170 elements of green competencies is between 0.00 and 0.55. The summary statistics show that the reliability of the items and the separation of the items of the green competencies instrument are 0.98 and 6.46, respectively. Similarly, the item reliability of each construct is between 0.96 and 0.99, and the reliability of the person is between 0.79 and 1.97, respectively. In terms of item fit statistics, a total of 157 items are found to be fit to achieve the objectives of the study. The result also indicates that the range of fit for the four (4) identified green competencies constructs is between 0.61 and 1.49 signifying that all the constructs are in harmony in measuring the items in the constructs, so suitable in achieving the objectives of the research.

Piecewise differentiation of the fractional order CAR-T cells-SARS-2 virus model

The pandemic caused by the SARS-CoV2 virus has prompted research into new therapeutic solutions that can be used to treat the CoVid-19 syndrome. As part of this research, immunotherapy, first developed against cancer, is offering new therapeutic horizons also against viral infections. CAR technology, with the production of CAR-T cells (adoptive immunotherapy), has shown applicability in the field of HIV viral infections through second generation CAR-T cells implemented with the “CD4CAR” system with a viral fusion inhibitor. In addition, to avoid the immunoescape of the virus, bi- or trispecific CAR receptors have been developed. Our research group hypothesizes the use of this immunotherapy system against SARS-CoV2, admitting the appropriate adjustments concerning the target-epitope and a possible remodeling of the nuclease related to the action of this virus. For a more in-depth analysis of this hypothesis, a mathematical model has been developed which, starting from the fractional derivative Caputo, creates a system of equations that describes the interactions between CAR-T cells, memory cells, and cells infected with SARS-CoV2. Through an analysis of the existence and non-negativity of the solutions, the hypothesis is stabilized; then is further demonstrated through the use of the piece-wise derivative and the consequent application of the formula of Newton polynomial interpolation.

Research on Training Base’s Expansion of Scientific Popularization Function in Higher Vocational Colleges

Based on the needs of regional industrial development and technical personnel training, the training base for technical skill as the core function has effectively enhanced the development of vocational education. However, at present, the training base in vocational colleges is mainly applied for students’ teaching, and the overall utilization rate is low. It is of great importance in the rational application of software and hardware resources onto the training base to widely carry out popular science activities. The importance of training base construction and scientific popularization is expounded. On this basis, the research team took the S&T racing training base as a case to study its expandable scientific popularization function. The practice results show that using the resources of the training base to carry out scientific popularization activities can greatly improve teenagers’ interest in automobile technology. More training bases in vocational colleges should expand the function of scientific popularization.

Multi-regional land disturbances induced by mineral use in a product-based approach: A case study of gasoline, hybrid, battery electric and fuel cell vehicle production in Japan

Resource use in the country of final demand has severe environmental impacts in other distant countries and regions through resource exploitation. In the narrative of telecoupling, the land disturbance caused by economy-wide international trade has been widely addressed using a multi-regional input-output analysis, but not sufficiently considering the telecoupled land use associated with a specific manufactured product. In this study, using the total material requirement, a new methodology is proposed for evaluating telecoupled land disturbances induced by manufacturing a given product in a given country. This product-focused method is a hybrid approach based on the integration of inventory data of a given product (bottom-up) and the global trade database (top-down). The developed methodology was applied to evaluate the multi-regional land disturbances induced by the production of gasoline vehicles (GVs), battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and fuel cell vehicles (FCVs) in Japan, as a case study. Finally, the implication is further discussed from the perspective of supply risk, governance, and corporate social responsibility. It was found that the transition of automobile technology in Japan results in changes in the countries and regions most exposed to the telecoupled land disturbances: i.e., from Australia (26% for GVs) to Chile (27% for BEVs) and South Africa (20% for FCVs). The algorithm developed is relatively simplified so that it can be readily implemented to determine the telecoupled land disturbances associated with any product in any country for a sustainable interaction in their mineral trade with other distant countries and regions.

SDVEC: Software-Defined Vehicular Edge Computing with Ultra-Low Latency

New paradigm shifts and 6G technological rev-olution in vehicular services have emerged toward unmanned driving, automated transportation, and self-driving vehicles. As the technology for autonomous cars becomes mature, real challeng-es come from reliable, safe, real-time connected transportation operations to achieve ubiquitous and prompt information exchanges with massive connected and autonomous vehicles. This article introduces novel wireless distributed architectures that embed the edge computing capability inside software-defined vehicular networking infrastructure. Such edge networks consist of open-loop grant-free communications and computing-based control frameworks, which enable dynamic eco-routing with ultra-low latency and mobile data-driven orchestration. Thus, this work advanc-es the frontiers of machine learning potential and next-generation mobile systems in vehicular net-working applications.

Software Design for Adaptive Laser Headlights

Abstract: The road traffic collisions and injuries, is still a major concern in automotive field. A disproportionate number of fatal accidents happen at nighttime. The major part of the collision is contributed by the human factors. Yet the technology in automobile is continuously assisting drivers while driving. Driving in the dark is not always easy as roads aren’t always illuminated, in such cases a powerful headlight is needed to illuminate most of the road. But such headlights cause glare to the oncoming traffic and again the chances of collision increase, with risking lives of passengers. Therefore, a need of smart headlamps which can illuminate the road far ahead without glaring the oncoming traffic is generated. This research aims to build a Laser based adaptive headlight system which can fulfil the need. The headlight design is proposed using Tinkercad software in which Arduino circuit has been used and software design is presented. The system works well with responding to the steering angle and controls the intensity of light preventing oncoming traffic from getting glared. Keywords: Adaptive headlight system, laser headlights, Arduino, Arduino software design, design of headlight system, cornering lights, effects of laser headlights, Tinkercad

Structural Analysis, Phase Stability, Electronic Band Structures, and Electric Transport Types of (Bi2)m(Bi2Te3)n by Density Functional Theory Calculations

Thermoelectric power generation is a promising candidate for automobile energy harvesting technologies because it is eco-friendly and durable owing to direct power conversion from automobile waste heat. Because Bi−Te systems are well-known thermoelectric materials, research on (Bi2)m(Bi2Te3)n homologous series can aid the development of efficient thermoelectric materials. However, to the best of our knowledge, (Bi2)m(Bi2Te3)n has been studied through experimental synthesis and measurements only. Therefore, we performed density functional theory calculations of nine members of (Bi2)m(Bi2Te3)n to investigate their structure, phase stability, and electronic band structures. From our calculations, although the total energies of all nine phases are slightly higher than their convex hulls, they can be metastable owing to their very small energy differences. The electric transport types of (Bi2)m(Bi2Te3)n do not change regardless of the exchange–correlation functionals, which cause tiny changes in the atomic structures, phase stabilities, and band structures. Additionally, only two phases (Bi8Te9, BiTe) became semimetallic or semiconducting depending on whether spin–orbit interactions were included in our calculations, and the electric transport types of the other phases were unchanged. As a result, it is expected that Bi2Te3, Bi8Te9, and BiTe are candidates for thermoelectric materials for automobile energy harvesting technologies because they are semiconducting.

Abnormal Noise Recognition of Door Closing for Passenger Car based on Image Processing

Background: With the rapid development of automobile technology, the problem of abnormal door-closing noise has attracted more and more attention. The abnormal door-closing noise is an important factor for judging the quality of a car, so accurate identification of abnormal noise is the premise of fundamentally solving the abnormal noise. Objective: To accurately identify the abnormal sound of car closing through the image processing method. Methods: To accurately identify the abnormal noise of car closing the method to recognize and classify images using Support Vector Machine (SVM) is proposed. This method extracts Histogram of Oriented Gradient (HOG), texture and Speed Up Robust Features (SURF). The three extracted feature vectors are combined and used as the input of SVM. The classifiers obtained by different kernel functions are used to predict the labels of the test set, respectively. Results: Calculating the ratio of the value on the diagonal of the confusion matrix to the total number of each row, and this ratio is the classification accuracy rate. Test accuracy rate is 85%, the results indicate that the accuracy is high. Conclusion: This paper uses image processing methods to extract HOG, GLCM, SURF features and merge them together as a new feature vector. The experimental results show that the SVM classifier using the Gaussian kernel function optimized by hyperparameters has a high accuracy rate and can be used to identify whether the door is closed with abnormal noise.

LIDAR Self Driving Car

Abstract: Increasing population is the major issue of transportation nowadays. People who live and work in the major cities of the world are faced with increasing levels of congestion, delays, total travel time, costs, frustration, accidents and loss of life. The objective of this project is to help prevent traffic accidents and save people’s time by fundamentally changing car use. The system would have sensors to detect the obstacles and to be able to react according to their position. In this project we have developed an automated driving system which drives the car automatically. We have developed a technology for cars that drives it automatically using LIDAR. This car is capable of sensing the surroundings, navigating and fulfilling the human transportation capabilities without any human input. It continuously tracks the surrounding and if any obstacle is detected vehicle senses and moves around and avoids the obstacle. An autonomous car navigation system based on Global Positioning System (GPS) is a new and promising technology, which uses real time geographical data received from several GPS satellites to calculate longitude, latitude, speed and course to help navigate a car. As we know the development of gps is more improved now the accuracy of gps we can see centimetre also so Like for our car to go at specific inputted location we use this gps technology.Lidar is used for sensing the surroundings. Like radar, lidar is an active remote sensing technology but instead of using radio or microwaves it uses electromagnetic waves. Keywords: Congestion, Traffic Accident, LIDAR sensor, Global Positioning System, Electromagnetic waves

Self-Driving Cars

Significant improvements in the last decade have greatly advanced self-driving car technology. These new capabilities will have profound global impacts that could markedly change society, not to mention the significant improvements they bring to the overall efficiency, convenience, and safety of our roadways and transportation systems. Addressing self-driving technology-related concerns is important, particularly given these broad potential impacts. In this paper, we are exploring the possibility of.

Impact of personality factors on purchase and adoption of consumer durables with advanced technology

PurposeThe purpose of this paper is to identify critical personality traits affecting and influencing buying behaviour in high involvement consumer durables. It also intends to guide practitioners in selecting appropriate marketing frameworks, consumer segments and processes considering the characteristics of consumer behaviour in developing economies.Design/methodology/approachIt systematically reviews the literature on consumer personality traits, its measurement and related consumer buying behaviour. It uses data collected from potential car buyers at various car showrooms across the Indian subcontinent. The authors have worked with the online survey firm Qualtrics, to gather a data set of 328 car purchase intenders’ responses to their validated survey. The model was tested using the SmartPLS.FindingsThe personality traits of imagination, agreeableness and social factors positively influenced attitude towards automobiles with advanced technology. Further, in line with the theory of planned behaviour, it is seen that a positive attitude towards advanced technology and design for automobiles makes a person more willing to pay for the same.Research limitations/implicationsThe study is confined to consumers intending to purchase a car, who are Indian residents.Originality/valueIt adds to the comparatively lesser body of study on the impact of personality traits on intentions and attitudes in high involvement consumer durable purchases. Further, it serves as an empirical examination of the adoption of new technologies, in the context of high involvement consumer durables. For practicing managers, it provides a reference for deciding future development directions and approaches related to the effective market launch strategies and commercialization of advanced technology automobiles in India.

Power Factor Increase of Electric Drives of Railway Shunting Winches

Purpose. The main purpose of the work is to develop recommendations for increasing the power factor of electric drives of railway shunting winches, taking into account the peculiarities of the technological process at loading and unloading sites. To achieve this purpose, the following tasks were set: to choose a rational way to increase the power factor of the electric drive of shunting winch, taking into account the peculiarities of the technological process, simplicity and reliability of the electrical circuit; develop a methodology (calculation algorithm) to determine the elements of the circuit, which are designed to increase the power factor; conduct research to determine the numerical technical characteristics of these elements, using the developed methodology; assess the power factor increase for the electric drive of some models of winches; formulate recommendations on possible modernization of the electric drive of shunting winches. Methodology. An algorithm for calculating the reactive power of an unregulated cosine capacitor, which has to be permanently connected to the terminals of a three-phase induction motor of a shunting winch drive in order to increase the power factor, is developed. Findings. Studies have shown that the proposed method of increasing the power factor gives a significant increase in different values of the load factor of the shunting winch motor, while ensuring the simplicity of the scheme and its reliability. Originality. For the first time, taking into account the peculiarities of the technology of unloading and loading cars, a method of calculating the required reactive power of an unregulated cosine capacitor, which is connected to the terminals of the drive motor, is proposed. The formula for determining the load factor of the electric motor depending on the ratio of unloaded and loaded cars that move with a winch is proposed. The formula for determining the load factor of the electric motor depending on the ratio of unloaded and loaded cars, which move using a winch is proposed. Practical value. The results are of practical value because the obtained numerical values of increasing the power coefficients of the drives of shunting winches and reactive power required for this cosine capacitors can be used as primary information in deciding whether it is advisable to modernize winch drives by connecting to the terminals of three-phase induction motor.

Multi-objective optimization for autonomous driving strategy based on Deep Q Network

Autonomous driving is an important development direction of automobile technology, and driving strategy is the core of the autonomous driving system. Most works in this area focus on single-objective tasks, such as maximizing vehicle speed or lane-keeping, and rare attention has been paid to the quality of driving skills. Therefore, a multi-objective learning method is proposed for autonomous driving strategy based on deep Q-network, where two optimization objectives are involved, i.e., vehicle speed and passenger comfort. An end-to-end autonomous driving model is designed by using vehicle front camera images as inputs to the Q-network and makes decisions based on the output Q values. Considering the vehicle speed and passenger comfort, the reward function is designed for multi-objective optimization. To evaluate the effectiveness of the method, training and testing are performed in a simulator, and a single-objective strategy with the goal of maximizing speed is designed for comparison. The results show that the proposed multi-objective autonomous driving strategy can strike a balance between vehicle speed and passenger comfort. Compared with the single-objective strategy, the multi-objective strategy has a significant improvement in comfort, while the average speed is only slightly reduced.