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Journal articles on the topic 'Smart vehicle'
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Raut, Sonali P., and A. C. Pise. "Smart Vehicle." Journal of Electronics,Computer Networking and Applied Mathematics, no. 41 (December 1, 2023): 38–47. http://dx.doi.org/10.55529/jecnam.41.38.47.
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This article gives a summary of the existing state of affairs and potential developments for smart vehicles while taking into consideration social, technological, and transportation aspects. Additionally, it examines the strategies for turning the smart into a generic vehicle, potential future developments, 5G, ADAS, and power source characteristics. This will make it possible for linked automobiles to take center stage in smart cities. Information may be exchanged between vehicles and road infrastructures as well as from one vehicle to another thanks to the vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication frameworks. It attempts to improve mobility, prevent or lessen auto accidents, and offer additional advantages for road safety. Motivations, open problems, and suggestions from other academics were taken into account to enhance and understand the various histories and characteristics of the business .All publications about data transfers in the V2I communication system were thoroughly searched. They use DSRC and 5G, Bluetooth and WIFI technology but there are many problems and data. I exploited RF frequencies to spontaneously broadcast the data in order to get around that.
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Bharathi, V. C. "Smart Parking System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1823–26. http://dx.doi.org/10.22214/ijraset.2021.36746.
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In the modern age, many people have vehicles. Vehicle is now a primary need. Every place is under process of urbanization. There are many supermarkets and shopping centers etc. There are many creative places where people used to go for refreshing and relaxation. All these places are full of with people so they need a parking space where people can park their vehicles safely and easily. Every parking area needs a website or system that records the detail of vehicles to give the parking facility. With the help of iot based system we can deliver a good service to users/people who wants to park their vehicles into organization’s premises. Present days in parking areas they just maintain the vehicles just with tokens and they have records of vehicle details in books so that during some critical situations like police enquiry of terrorist car or vehicle missing that case it is difficult to find the details of particular vehicle. But with our parking management system it is easy to find within 1 to 2 seconds. By parking the vehicle in public place the vehicle can be claimed by other person but in this case there is no such problem and no need to give fine for anything we can park our vehicle with securely.
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Sai Teja, Pendur, Nagireddy Shiva Smaran Reddy, Praveen Kumar Pandugu, and Pratheek Vangari. "Smart Vehicle Monitoring And Tracking System." E3S Web of Conferences 391 (2023): 01099. http://dx.doi.org/10.1051/e3sconf/202339101099.
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Nowadays tracking a theft vehicle or monitoring continuously vehicles, tracking systems have escalated quickly. The major concern of the proposed system is identifying vehicle theft and monitoring its status. We can use this in several ways such as delivering security to vehicles such as bikes or cars and many other vehicles and if there are any goods in the vehicle, with the help of this we can keep track of the vehicle in maps. This is very useful for tracking the movement of a vehicle from any location at any time. In this, we can make a tracking system that is modelled and executed for tracking the signal of any enabled vehicle from any geographical location.
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Fadhil, Ali Muayed, Norashidah Md Din, Norazizah Binti Mohd Aripin, and Ali Ahmed Abed. "Advanced Privacy Scheme to Improve Road Safety in Smart Transportation Systems." International journal of Computer Networks & Communications 16, no. 2 (2024): 71–86. http://dx.doi.org/10.5121/ijcnc.2024.16205.
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In -Vehicle Ad-Hoc Network (VANET), vehicles continuously transmit and receive spatiotemporal data with neighboring vehicles, thereby establishing a comprehensive 360-degree traffic awareness system. Vehicular Network safety applications facilitate the transmission of messages between vehicles that are near each other, at regular intervals, enhancing drivers' contextual understanding of the driving environment and significantly improving traffic safety. Privacy schemes in VANETs are vital to safeguard vehicles’ identities and their associated owners or drivers. Privacy schemes prevent unauthorized parties from linking the vehicle's communications to a specific real-world identity by employing techniques such as pseudonyms, randomization, or cryptographic protocols. Nevertheless, these communications frequently contain important vehicle information that malevolent groups could use to Monitor the vehicle over a long period. The acquisition of this shared data has the potential to facilitate the reconstruction of vehicle trajectories, thereby posing a potential risk to the privacy of the driver. Addressing the critical challenge of developing effective and scalable privacy-preserving protocols for communication in vehicle networks is of the highest priority. These protocols aim to reduce the transmission of confidential data while ensuring the required level of communication. This paper aims to propose an Advanced Privacy Vehicle Scheme (APV) that periodically changes pseudonyms to protect vehicle identities and improve privacy. The APV scheme utilizes a concept called the silent period, which involves changing the pseudonym of a vehicle periodically based on the tracking of neighboring vehicles. The pseudonym is a temporary identifier that vehicles use to communicate with each other in a VANET. By changing the pseudonym regularly, the APV scheme makes it difficult for unauthorized entities to link a vehicle's communications to its real-world identity. The proposed APV is compared to the SLOW, RSP, CAPS, and CPN techniques. The data indicates that the efficiency of APV is a better improvement in privacy metrics. It is evident that the AVP offers enhanced safety for vehicles during transportation in the smart city.
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K R, Suma. "Smart Vehicle Sensor System." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 4–7. http://dx.doi.org/10.22214/ijraset.2021.37004.
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Over recent years production of the vehicle around the world has increased rapidly, vehicle theft has become a shared concern for all citizens. Security and safety have always become a necessity. However, present anti-theft systems lack the tracking and monitoring function. The Wi-Fi module enabled cost-effective solution has been made to protect the vehicles. This paper attempts to utilize two physically disjoint units in conjunction with each other, to provide a fool-proof mechanism against vehicle theft. A prototype has been made using Arduino and Wi-Fi module. Android smartphones are used to design for the user interface that allows access of the vehicle to an intended person only.
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Pradnya, Kapadne, Mehetre Sumit, Garpal Athrav, Kankate Vaishanvi, and Koli Sanjay. "Smart Bike Using IoT." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 636–39. https://doi.org/10.5281/zenodo.7950995.
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People use different vehicles and mean for commuting around the world, but motorcycles are the craziest vehicle used by the young generation. Among different commuting vehicles, motorcycles dominate the roads in many low- and middle-income countries and at the same time, there is a rapid increase in the injuries and deaths of riders. Recentlyin October 2022, the World Health Organization [WHO] and itspartners launched an updated manual to help policymakers in ending the disaster of road traffic deaths and injuries involving motorcycles and other powered two- and three-wheelers. The report says nearly 30% of all road crash deaths involve poweredtwo- and three-wheeled vehicles, such as motorcycles, mopeds, scooters, and electrical bikes (ebikes), and the numbers are rising. Key risk factors for motorcycle traffic injuries include drivers not wearing helmets, overspeeding, rash driving, alcohol impairment, mixed traffic conditions, violation of traffic rules, a lack of protection from the vehicle in a crash, anda lack of safe infrastructure for such vehicles such as poor road surfaces and roadside hazards. The authors have proposed a 'smart bike' based on the use of new edge technologies such as IoT and a powered two-wheeled vehicle equipped with sensors, and an SoS alarming system to get help in emergencies. The Global Positioning System (GPS)has become an integral part of a vehicle system and its capabilities can be used to monitor and control the speed of a vehicle. Also, GPS will help to locate the vehicle in case of emergencies which will ensure in-time help of emergency service providers to save valuable human life.
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M, Vidyashankar. "Enhancing Public Safety and Traffic Management: A Machine Learning Solution for Missing Vehicles and Smart Signaling." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 842–45. https://doi.org/10.22214/ijraset.2025.66978.
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This paper presents a system for missing vehicle tracking and smart traffic signaling using machine learning, embedded systems, RFID, and OpenCV. The missing vehicle tracking component uses RFID tags and readers at strategic locations to identify and track vehicles. Upon a theft report, the system activates tracking, utilizing RFID data and potentially integrating with other tracking mechanisms (e.g., GPS if available) to locate the vehicle. The smart signaling system employs OpenCV and cameras to analyze real-time traffic flow. Machine learning algorithms process this visual data to predict congestion and dynamically adjust signal timings for optimized traffic flow. The system integrates these two aspects: in the event of a stolen vehicle being identified via RFID, the smart signaling system can prioritize traffic flow in the vehicle's direction, aiding in recovery. This combined approach aims to reduce vehicle theft, improve traffic management, enhance safety, and contribute to cost savings through optimized traffic flow and faster vehicle recovery.
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Leeks, Harry. "Smart Electric Vehicle Charging." ITNOW 61, no. 4 (2019): 12–13. http://dx.doi.org/10.1093/itnow/bwz092.
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Abstract What does IT have to do with the charging of electric vehicles? In this article, Harry Leeks, a graduate IT Analyst at National Grid, explains how IT plays a pivotal role in the electric vehicle charging market.
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Narmadha, R., R. Madhav, D. Barath, S. Kiruthika, and J. Keerthana. "Smart Moving Vehicle Detection System." Journal of Computational and Theoretical Nanoscience 17, no. 4 (2020): 1758–63. http://dx.doi.org/10.1166/jctn.2020.8438.
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In Vehicle detection is a computer skill that determines the locations, direction and speed of running vehicles in arbitrary (digital) images. Using vehicle features and ignores anything else, such as buildings, trees and bodies. Vehicle detection is currently an active research area in the computer vision community. Automobile localization and detection are frequently the primary step in bids such as face gratitude, video observation, vehicle computer interface and image database administration. Speed and tracking vehicle shapes is a prerequisite for recognition and/or vehicle features analysis, although it is often assumed that a normalized moving vehicle image is available. Machine Learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. In this paper, a machine learning algorithm created for analyzing vehicle detection, travel direction and speed measurement.
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Phillips, Anthony M., Ryan A. McGee, Johannes G. Kristinsson, and Hai Yu. "Smart, Connected and Electric." Mechanical Engineering 135, no. 03 (2013): S4—S9. http://dx.doi.org/10.1115/1.2013-mar-4.
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This article introduces opportunities that are seen at the intersection of electrification, connectivity, and smart controls in the automobile industry. Computational Intelligence provides the vehicle the ability to reason, adapt, and learn based on historical usage data, the present operating conditions, and the predicted future states. Modern automobiles continue to grow in complexity and sophistication. Electrified powertrains now provide vastly improved fuel efficiency by utilizing high-voltage systems to overcome some of the shortcomings of traditional combustion engines. Smart controls have enabled a wealth of new vehicle features ranging from automatic climate control to vehicle dynamic control. Vehicle connectivity, having already empowered the driver through infotainment and telematics, now promises new computing resources and information that can be leveraged directly for improved vehicle performance. At the intersection of these three vehicle mega trends lies a field that is rich for development. In the future, drivers will benefit in everything from enhanced drivability to more durable vehicles.
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Gupta, Rajesh Kumar, L. N. Padhy, and Sanjay Kumar Padhi. "Smart Driving System for Improving Traffic Flow." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (2017): 236. http://dx.doi.org/10.23956/ijarcsse/v7i7/0174.
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Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay. For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.
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YAO, Yiwei, Yongjun WANG, Xiaoyan ZHANG, et al. "Smart vehicle for smart city." SCIENTIA SINICA Informationis 46, no. 5 (2016): 551–59. http://dx.doi.org/10.1360/n112015-00294.
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Ravi Kumar Palla, Chonika Molli, Gowthami Narayanasetti, Dwarakesh Malla, Sai kiran Katiki, and Venkata Mani Gopal. "Vehicle parking: A smart solution." Global Journal of Engineering and Technology Advances 13, no. 3 (2022): 066–71. http://dx.doi.org/10.30574/gjeta.2022.13.3.0209.
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The system proposed in this paper provides an effective solution in finding the vacant space and manages the vehicles entering in and out of parking area. The system consists of Arduino Uno, Liquid Crystal Display (LCD), Servo motor and Infrared (IR) Sensors. Two IR Sensors placed just before and after the entrance of the gate which detects the motion of the vehicle either entering or leaving the gate or slot area. The Arduino Uno, a microcontroller counts the number of available slots based on the entry or exit of the vehicle and the state of each slot i.e., either the slot is empty or full and provides a feedback which is displayed on the LCD. This whole process makes the system fully automated and thus provides a solution for vehicle parking in a smart way. The proposed automated system causes the driver to navigate in reaching to the vacant slot using display thus reduces search time. The proposed system can be implemented at places where heavy number of vehicles needs to be parked.
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Ravi, Kumar Palla, Molli Chonika, Narayanasetti Gowthami, Malla Dwarakesh, kiran Katiki Sai, and Mani Gopal Kammila Venkata. "Vehicle parking: A smart solution." Global Journal of Engineering and Technology Advances 13, no. 3 (2022): 066–71. https://doi.org/10.5281/zenodo.7680584.
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The system proposed in this paper provides an effective solution in finding the vacant space and manages the vehicles entering in and out of parking area. The system consists of Arduino Uno, Liquid Crystal Display (LCD), Servo motor and Infrared (IR) Sensors. Two IR Sensors placed just before and after the entrance of the gate which detects the motion of the vehicle either entering or leaving the gate or slot area. The Arduino Uno, a microcontroller counts the number of available slots based on the entry or exit of the vehicle and the state of each slot i.e., either the slot is empty or full and provides a feedback which is displayed on the LCD. This whole process makes the system fully automated and thus provides a solution for vehicle parking in a smart way. The proposed automated system causes the driver to navigate in reaching to the vacant slot using display thus reduces search time. The proposed system can be implemented at places where heavy number of vehicles needs to be parked.
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Youcef, BOT, YOUSFI Abdelkader, and ALLALI Ahmed. "Smart Control of the Bidirectional Energy Exchange of Electric Vehicles With the Electrical Network." INTERNATIONAL JOURNAL OF ADVANCED STUDIES IN COMPUTER SCIENCE AND ENGINEERING (IJASCSE) Vol.11, No.12 (2022): 29–35. https://doi.org/10.5281/zenodo.7598791.
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<strong>INTERNATIONAL JOURNAL OF ADVANCED STUDIES IN COMPUTER SCIENCE AND ENGINEERING (IJASCSE)</strong> VOLUME 11 ISSUE 12, 2022 <em><strong>Abstract:</strong> The democratization of plug-in hybrid vehicles as well as purely electric vehicles implies a surplus of demand on the distribution networks. Vehicle-to-Grid aims to meet this increased demand by using vehicles no longer as simple loads for the electricity network but as players carrying out two-way energy exchanges. The work presented in this article proposes a real-time “Grid-to-Vehicle/Vehicle-to-Grid” control algorithm for an electrical distribution system. The results show that the system makes it possible to achieve energy gains shared between the actors while efficiently recharging the participating vehicles.</em> <em><strong>Keywords:</strong> Electric vehicles; Distribution networks; Smart Grids; Vehicle-to-Grid; Smart control.</em>
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Roseline, Dr S. "Resolving Complexities in the Integration of Autonomous Vehicle." International Scientific Journal of Engineering and Management 03, no. 04 (2024): 1–9. http://dx.doi.org/10.55041/isjem01660.
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Smart vehicles become increasingly prevalent, there is a growing need for sophisticated monitoring systems that go beyond basic parameters. Current Smart EV monitoring systems often lack features that address critical aspects such as engine temperature, drunk and drive prevention, and immediate accident response. There is a pressing need for an all-encompassing monitoring system that enhances both vehicle performance and user safety. This project introduces an advanced Smart E-Vehicle Monitoring System designed to offer a comprehensive suite of features, including engine temperature monitoring, drunk and drive testing, accident detection, and remote control via mobile devices. The system not only ensures optimal vehicle performance but also prioritizes user safety through proactive anomaly detection. Keywords: Smart Vehicles, Electric Vehicles, monitoring systems, performance, Engine temperature, mobile devices.
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Ansari, t. Shamaila. "Smart Vehicle Assistant." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43302.
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Driver safety is a prime challenge in present day transportation systems, in which factors inclusive of sleep, sur- roundings, and vehicle dynamics considerably have an impact on site visitors accidents. This paper provides an incorporated machine, with AI and includes a proposal that integrates superior pc imaginative and prescient, system studying algorithms and real-time for statistics processing In addition to dates detected through landmark evaluation, vehicles that detection using latest object detection models inclusive of YOLO and lane departure warnings the usage of convolutional neural networks (CNNs), the system includes photo enhancement to overcome challenges which includes light a address ground conditions and environmental modifications. Extensive checking out and evaluation demon- strates the robustness, scalability and flexibility of the system to actual-international situations, massively improving accuracy and reaction time This painting lays the foundation for sensible, secure using solutions and contributes to the development of self sustaining automobile technology. Keywords- Vehicle Detection, Lane Departure Warning, Object Detection Algorithms, YOLO (You Only Look Once), Convolutional Neural Networks (CNNs), Facial Landmark Analysis, Real-Time Image Processing, Autonomous Vehi- cles, Low-Light Image Enhancement, Machine Learning for Transportation, AI in Driver Assistance Systems, Deep Learning in Automotive Applications, Traffic Safety Tech- nologies.
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RAM VARA PRASAD, B., CH PRASANTHI, G. JYOTHIKA SANTHOSHINI, K. J. S. V. KRANTI KUMAR, and K. YERNAIDU. "SMART ELECTRICAL VEHICLE." i-manager's Journal on Digital Signal Processing 8, no. 1 (2020): 7. http://dx.doi.org/10.26634/jdp.8.1.17347.
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Balog, Michal, Žofia Šimeková, and Pavol Semančo. "Smart Vehicle Railroad." Applied Mechanics and Materials 708 (December 2014): 148–52. http://dx.doi.org/10.4028/www.scientific.net/amm.708.148.
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Radio Frequency Identification (RFID) technology introduces the way of automated data collection, and processing to improve accuracy of processed data. In the present time possibilities in development and application of RFID technologies are almost limitless. Increasing expansion of RFID technology in almost any industry, where the RFID tag can be put on any product or material or component is an evidence of the previous assertion. Apart from technical aspects, i.e. security, the financial effect of the RFID technology implementation is also relevant in rail freight transport. Using this technology to keep records of technical condition of the wagons can be avoided a huge disaster caused by lack of knowledge about technical condition of the wagon. It can bring the return of initial investment and cost reduction.
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S.Bharath, Ram. "SMART VEHICLE PARKING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 3 (2016): 420–23. https://doi.org/10.5281/zenodo.47572.
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The goal of this project is to count the number of empty car parking areas and to display them in a Website. This system consists of sensors attached to several parking areas. These sensors located in different parking area’s detects the presence of vehicle and sends information to Microcontroller, which calculates the number of available empty parking areas and uploads them in a website. This basically works on the principle of Internet of Things here the sensors are connected to internet.
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Jung, In Hwan, Jae Moon Lee, and Kitae Hwang. "Smart Parking Management System Using AI." Webology 19, no. 1 (2022): 4629–38. http://dx.doi.org/10.14704/web/v19i1/web19307.
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This paper is aimed to introduce a smart parking lot management system using multiple cameras and artificial intelligence technique. When a vehicle enters a parking lot, it recognizes the vehicle number using embedded camera, tracks which parking space the vehicle is parked in, and updates parking space information. In addition, using a surveillance camera images, it has been also implemented to detect collision accidents that may occur while the vehicle is moving in the parking lot. Vehicle number recognition system uses OCR technique and is implemented on a Raspberry system. By managing the vehicle number recognized at the entrance of the parking lot as an Object ID, it was possible to effectively track the vehicle as a moving object inside the parking lot and finally identify the parking location. In order for accident detection, YOLO with CNN deep learning process is used. More than 500 possible collision images are trained in advance. Experimental results show that the detection accuracy of parking and accident detection increases as the number of training images increases. The accident detection needed more training images because it has more diversity. By using the smart parking system implemented in this paper, it is possible to effectively manage the vehicle's parking location, free space information and possible accidents. Using a cloud system, implemented system can provide drivers an integrated parking lot information over large areas.
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Tarulescu, Radu, Stelian Tarulescu, Cristian Leahu, and Marius Olaru. "Photovoltaic system for E-Smart electric vehicle." IOP Conference Series: Materials Science and Engineering 1220, no. 1 (2022): 012009. http://dx.doi.org/10.1088/1757-899x/1220/1/012009.
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Abstract The vehicles relied on fossil fuels are rapidly being replaced by electric and plug-in hybrid vehicles. But these types of vehicles are still faced with the problem of energy availability. The abundance of solar radiation and its use as the power source in electric vehicles is a necessary condition for environmental pollution limitation. In this study, the authors present photovoltaic systems used as an electricity supply for E-Smart electric vehicles. E-Smart is an electric vehicle obtained through conversion, of a Smart ForTwo City vehicle, from the internal combustion propulsion system to a system that uses a three-phase asynchronous motor supplied from a pack of 32 batteries of LiFePO4 type.
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Tu, Haoran. "An overview of the development of intelligent materials and active vibration isolation systems for vehicles." Journal of Physics: Conference Series 2649, no. 1 (2023): 012020. http://dx.doi.org/10.1088/1742-6596/2649/1/012020.
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Abstract Intend to reduce the impact of external vibrations on the smoothness of automotive precision instruments and vehicles and to improve the operational accuracy of automotive precision instruments and the comfort of passenger vehicles, vibration isolation technologies are used and active control systems are applied to vibration isolation systems. The extensive use of smart materials in vibration isolation systems has enabled the design of vehicle structures based on smart materials to meet the suppression of vehicle vibrations well. This paper reviews the application of commonly used smart materials to the field of vehicle vibration damping, summarizes the configuration design of single-axis and multi-axis vibration isolation systems, and develops the thinking behind the application of active control technology to other carriers, pointing out the future research direction of current smart materials for vehicles.
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Chopra, Varun. "Cyber Intelligence in Smart Vehicles." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 3 (2021): 144–51. http://dx.doi.org/10.35940/ijrte.c6464.0910321.
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In the embryonic stage, the usage of vehicle tracking systems were primarily restricted to getting the geographical location of the vehicular units. This scenario, however, was not perennial and with the escalation from a rudimentary stage to a highly complex archi- tecture for vehicular administration that we witness today, the standards for the vehicles security have also become monumental. With the development of V2X communications, the gamut of facilities provided by smart vehicle services has expanded prodigiously. These technological advancements, however have come at a cost. The gargantuan transition that has taken place over the recent years exacts a lot of security and safety mechanisms to be implemented, adjunct to the products and services it comes equipped with. In this paper, after a comprehensive study in the domain, we imply a security system model comprising of a Microcontroller Unit (MCU), as a part of the Vehicle Tracing Mechanism (VTM), well connected with a Management Hub. The communications be- tween the Vehicular Unit(s), Management Hub and the system Vehicle Tracing Mecha- nism (VTM) are made viable via V2X communications with conducive aid from technolo- gies like Global Positioning System, Radio Frequency Identifications and GPRS network. The paper aims to ameliorate the extant security protocols and improve the security and safety standards of smart vehicles by broaching cyber intelligence in smart vehicles.
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Varun, Chopra. "Cyber Intelligence in Smart Vehicles." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 3 (2021): 144–51. https://doi.org/10.35940/ijrte.C6464.0910321.
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In the embryonic stage, the usage of vehicle tracking systems were primarily restricted to getting the geographical location of the vehicular units. This scenario, however, was not perennial and with the escalation from a rudimentary stage to a highly complex archi- tecture for vehicular administration that we witness today, the standards for the vehicles security have also become monumental. With the development of V2X communications, the gamut of facilities provided by smart vehicle services has expanded prodigiously. These technological advancements, however have come at a cost. The gargantuan transition that has taken place over the recent years exacts a lot of security and safety mechanisms to be implemented, adjunct to the products and services it comes equipped with. In this paper, after a comprehensive study in the domain, we imply a security system model comprising of a Microcontroller Unit (MCU), as a part of the Vehicle TracingMechanism (VTM), well connected with a Management Hub. The communications be- tween the Vehicular Unit(s), Management Hub and the system Vehicle Tracing Mecha- nism (VTM) are made viable via V2X communications with conducive aid from technolo- gies like Global Positioning System, Radio Frequency Identifications and GPRS network. The paper aims to ameliorate the extant security protocols and improve the security and safety standards of smart vehicles by broaching cyber intelligence in smart vehicles.
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B, Sandeep, Dr K. S. Keerthiprasad, Manuraj D C, Rakshith Gowda M, and B. L. Manohara. "Smart Parking System." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 589–99. http://dx.doi.org/10.22214/ijraset.2023.54718.
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Abstract: In many situations, the people owning vehicles should leave their vehicles parked for couple of days to travel by other means of transportation (Flights from International Air ports). Sometimes the vehicles get damaged and they even get stolen. The vehicles needed to be parked in specified area safety. Keeping the above problem in mind, our research work proposes an idea of smart parking system. The user shall be given a slot and an admin card of the parking lot after making the online payments. The user can use the admin card to access the parking lot. The door opens if the admin card is used. The parking lot shall also be equipped with firefighting system, infrared sensors, motion sensor with efficient signaling/feedback system interlinked to Communication devices, so as to detect if incase of fire or someone breaks into the parking lot, intended to steal or make damages to the vehicle. The smart parking system sends alert message with live streaming to the admin/user/customer’s Mobile or any Communication devices making him/her aware of the situation and initiate right action to protect the vehicle from further destruction.
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Shrishail Hatti. "A Study on Latest trends in Automobile Industries with Reference to Electric Vehicles and Smart Grids." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 08 (2024): 2779–85. http://dx.doi.org/10.47392/irjaem.2024.0404.
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Automobile is trending now a day because every use personal vehicle for travelling and this paper reveals some of the aspects about the one of the personal vehicles i.e Electric Vehicle and further about Smart grids for that electric vehicle. This is about how Electrical Vehicles can contribute to grid stabilization, simulation-based research for smart charging, grid communication, block chain based technology for Electric Vehicles with the purpose of achieving the international environmental and sustainable goals. Smart grid and future electric vehicle is the most emerging issues that are integrating in the near future. With the increase numbers of EV’s, new challenges are imposed to the grid, in terms of synergistic, continuous, dynamic, and stable integration of electric mobility problems. What was impossible to achieve back in history, eliminating Electrical Vehicles from the market due to its disadvantages is now possible via the Smart Grid integration. This paper presents a review of electrical vehicles and the novel proposals on how to smartly integrate it into the Smart Grid. Moving forward the future characteristic of a smart grid includes, flexibility being able to adapt to the changing needs that a system could require, clever and safe these are the values of the smart grid, efficient where minimizing new infrastructure for electrical grid is the aim, open to be integrated with renewable energies safely, and finally sustainable a key point to the future environment and sociable acceptance. Due to the world vision of smart grid, things are changing rapidly.
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Memon, Nisar, Shahzad Memon, Khalil Khoumbati, and Mudasir Ahmed Memon. "A Comprehensive Security Risk Assesment Framework SRAF_IoV for Internet of Vehicles." Asian Bulletin of Big Data Management 4, no. 3 (2024): 130–42. http://dx.doi.org/10.62019/abbdm.v4i3.227.
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The advancement in digital world have led to the development of smart vehicles that connect to the Internet of Vehicles (IoV) Infrastructure. These smart vehicle also connects and communicate with other vehicles, and their connected components. Once connected, they can share information such as speed, direction, and distance between vehicles.IoV enhances traffic infrastructure by reducing congestion and incidents, ultimately improving road safety within highways and metropolitan cities. However, when smart vehicles use IoV to share data through vehicle-to-vehicle (V2V), Vehicle to Infrastructure (V2I), and Vehicle to everything (V2X) they also introduce security risks and vulnerabilities to the IoV Infrastructure. Malicious actors can exploit these vulnerabilities to alter data within the connected units of IoV infrastructure, such as electronic control units, onboard units, control networks, and sensors. Securing data within the IoV ecosystem is crucial to prevent malicious activity that could compromise confidentiality, integrity, and availability. In this study, a security risk assessment framework for IoV (SRAF-IoV) has been developed to identify vulnerabilities within the IoV network. SRAF-IoV that assesses security risks in connected vehicles. It evaluates vulnerabilities and assigns a risk level based on different communication channels (V2V, V2I, and V2X). The proposed framework was tested against various cyber-attacks to determine risk levels associated with different attack types (low, medium, high, and critical). The performance of SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV can push the smart vehicle industry to make critical security decisions during the development of smart vehicles.
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Rathod, Nirmit. "Traffi: Smart Surveillance for Roads." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2242–48. https://doi.org/10.22214/ijraset.2025.67767.
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Vehicle detection and counting play a crucial role in intelligent transportation systems, traffic monitoring, and urban planning. Traditional methods for vehicle detection often struggle with accuracy and real-time performance, especially in dynamic environments. With the advancement of deep learning, object detection models like YOLOv4 (You Only Look Once) have significantly improved detection speed and accuracy. Coupled with OpenCV, a powerful computer vision library, YOLOv4 enables efficient vehicle detection and tracking in real-world scenarios. In this work, we implement a vehicle detection and counting system using the YOLOv4 deep learning model and OpenCV. The system processes video streams to detect vehicles, classify them, and count their movement across predefined regions. YOLOv4's convolutional neural network architecture allows for high-speed inference, while OpenCV handles image preprocessing, post-processing, and visualization. The model is trained on a dataset of various vehicle types and optimized for real-time performance on both CPU and GPU environments. Our implementation achieves high accuracy in vehicle detection and counting, even in challenging conditions such as occlusions, varying lighting, and heavy traffic. The system demonstrates real-time processing capabilities, making it suitable for smart traffic management applications. By leveraging YOLOv4 and OpenCV, we provide a robust and efficient solution for automated vehicle monitoring, contributing to improved traffic flow analysis and transportation planning.
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Chen, Yuchu, Chang Liu, and Ruicong Wang. "Smart Design of Modern Electric Vehicles." Highlights in Science, Engineering and Technology 37 (March 18, 2023): 55–63. http://dx.doi.org/10.54097/hset.v37i.6039.
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This article is about the battery of electric vehicles since the battery is the power of the electric vehicle, which is the heart of the electric vehicles. The battery takes an important place in electric vehicles, also the main difference between gasoline vehicles is one has gasoline another one has a battery. Battery control almost all the things on the electric vehicles, it also included the speed and continuation which SUVs, that kind of vehicles can run far away than the normal internal combustion vehicles; Noise reducing using the Model Y materials for the electric vehicles to reduce the noise made from the electric vehicles and is quieter; and lastly safety of the electric vehicles, so if the electric vehicles are easy to have the car accident too often no one is going to buy this electric vehicle and to affect the safety of the electric vehicles, people have to come back to the start which is battery control the safety of the electric vehicles.
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S, Thylashri, Manikandaprabu N, Jayakumar T, Vijayachitra S, and Kiruthiga G. "Effective Techniques for Pedestrian Detection in Smart Autonomous Vehicles." Webology 18, no. 05 (2021): 1176–83. http://dx.doi.org/10.14704/web/v18si05/web18298.
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Pedestrians are essential objects in computer vision. Pedestrian detection in images or videos plays an important role in many applications such as real-time monitoring, counting pedestrians at various events, detecting falls of the elderly, etc. It is formulated as a problem of the automatic identification and location of pedestrians in pictures or videos. In real images, the art of pedestrian detection is an important task for major applications such as video surveillance, autonomous driving systems, etc. Pedestrian detection is also an important feature of the autonomous vehicle driving system because it identifies pedestrians and minimizes accidents between vehicles and pedestrians. The research trend in the field of vehicle electronics and driving safety, vision-based pedestrian recognition technologies for smart vehicles have established themselves loudly or slowing down the vehicle. In general, the visual pedestrian detection progression capable of be busted down into three consecutive steps: pedestrian detection, pedestrian recognition, and pedestrian tracking. There is also visual pedestrian recognition in the vehicle. Finally, we study the challenges and evolution of research in the future.
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Selden, Pema, Dechen Wangmo, Kinley Tenzin, Bijay Kumar Darjee, and Tsheten Dorji. "Smart Vehicle Identification System using RFID Technology." Zorig Melong | A Technical Journal of Science, Engineering and Technology 5, no. 1 (2021): 13–17. https://doi.org/10.17102/zmv5.i1.003.
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With increase in number of vehicles in Bhutan, some of the major problems faced are the waste of the time and effort of drivers and concern officials on duty for doing manual registration of the vehicle details at the checkpoints and also it is difficult to keep track of the records related to vehicles passing through these entry points for national security reasons. This report presents the development of a prototype of a Smart Vehicle Identification System using RFID technology and its key functionalities. This smart system is proposed as a solution to be used for providing effective and efficient services of vehicle registration at the entry gate of any organization’s premises or at the immigration checkpoints along the national highway in Bhutan. A prototype of the proposed smart system was developed as a web based application using Medoo Framework and Raspberry Pi for automatic identification of vehicles passing through the gate with RFID reader. All information related to vehicles including their owners and drivers are stored and managed using MySQL database server. The functionalities of the proposed smart system prototype are tested successfully on the development platform.
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Wan, Liangtian, Mingyue Zhang, Lu Sun, and Xianpeng Wang. "Machine Learning Empowered IoT for Intelligent Vehicle Location in Smart Cities." ACM Transactions on Internet Technology 21, no. 3 (2021): 1–25. http://dx.doi.org/10.1145/3448612.
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Intelligent Transportation System (ITS) can boost the development of smart cities, and artificial intelligence and edge computing are key technologies that support the implementation of ITS. Vehicle localization is critical for ITS since the safety driving and location-aware serves highly depend on the accurate location information. In this article, we construct a vehicle localization system architecture composed of multiple Internet of Things (IoT) with arbitrary array configuration and a large amount of vehicles in smart cities. In order to deal with the coexisting of circular and non-circular signals transmitted by vehicles, we proposed several vehicle number estimation methods for non-circular signals. Based on the machine learning technique, we extend the vehicle number estimation method into mixed signals in more complex scenario of smart cities. Then the DOA estimation method for non-circular signals based on IoT is proposed, and then the performance of this method is analyzed as well. Simulation outcomes verify the excellent performance of the proposed vehicle number estimation methods and the DOA estimation method in smart cities, and the vehicle positions can be achieved with high estimation accuracy.
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N, Shalini. "Smart Remote Transit Vehicle Monitoring with Emission Alert and Secured Access Using IOT." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 4192–95. http://dx.doi.org/10.22214/ijraset.2023.54104.
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Abstract: The research paper focuses on addressing automobile theft by proposing a framework utilizing the Internet of Things (IoT), GPS, and GSM technologies. This system allows vehicle owners to track and monitor their vehicles at any time from anywhere, improving security against professional thieves. The IoT sends information about the vehicle's status to the guardian or owner, while GPS provides the vehicle's current location. In case of theft, the fuel supply can be gradually cut off by sending a message, disabling the vehicle, and updating data to the Google Firebase server. An RFID card is provided to start the vehicle, and an Android app is developed using the MIT AppInventor tool for monitoring maps and notifications. Additionally, emission levels are measured and updated to the cloud, and a panic button is included for driver assistance in emergencies. This proposed system enhances existing anti-theft measures while offering innovative features for better vehicle security and management.
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Mansour, Ayman M. "Cooperative Multi-Agent Vehicle-to-Vehicle Wireless Network in a Noisy Environment." International Journal of Circuits, Systems and Signal Processing 15 (February 22, 2021): 135–48. http://dx.doi.org/10.46300/9106.2021.15.15.
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With the rapid development of vehicle communication and the goal of self-driving vehicle, research in this area is still ongoing, as car companies aspire for more studies and effective communication methods between vehicles. In this research, we have developed an intelligent, innovative and fully integrated multi agent model, which is used for vehicle-to-vehicle communications. The developed model is supported by an intelligent system based on a Nonlinear External Neural Network (NARX) and signal estimation theory. The system is built using real vehicles sensors, Arduino, GSM and RF technologies. The system is tested by applying different scenarios and observing vehicle behaviors. The results show that the smart system is able to make the appropriate decision based on both the vehicle's current condition and sensor readings. The developed system is able to operate effectively in a noisy environment in an excellent manner.
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Allam, Mohammed Shaibaz. "Smart Vehicle Service Management System Using IoT." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 3872–77. http://dx.doi.org/10.22214/ijraset.2022.44767.
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Abstract: To ensure the proper operation and efficient operation of the vehicle, regular vehicle service is necessary to monitor various parts of the vehicle. This paper “Smart Vehicle Service Management System using IoT” is being proposed to detect some problems in vehicle and also it provides a platform to book online service to the vehicles. It sends an alert message in form of SMS to the user if any problem is detected and suggests that servicing to vehicle is now required. The system is integrated with different sensors like FSR, adxl, DHT11, level etc to detect problem in vehicle and being displayed in LCD, so if any problem is detected the buzzer will give alert sound indicating the vehicle has a problem. Along this a Node MCU and GSM module is being placed to get live updates in cloud and also in a form of SMS and the same data is stored data in cloud. The alert message received in form of SMS is redirected to the SVSMS website which will allow the user to book online service. The proposed system helps the user to detect the problems in prior and act precisely so that efficiency of the vehicle is maintained and cost/ inconvenience could be avoided.
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SMART, ROAD SYSTEM FOR ELECTRIC VEHICLE. "SMART ROAD SYSTEM FOR ELECTRIC VEHICLE." Electric vehicle, Battery, Roller, Dynamo, Led. 5, no. 4 (2018): 72–76. https://doi.org/10.5281/zenodo.1234917.
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The main objective of Smart Road for Electric Vehicle System is to charge the battery of electrical vehicle running on Smart road by using wireless charging. The term refers to the ability to charge a vehicle while it is in motion. The objective is to demonstrate charging of electric and plug-in vehicle batteries, taking into account infrastructure challenges and communication requirements between the vehicles and charging sources with the purpose to extend the range of the vehicle. In 2012, the US Energy Information Administration reported that the US imported about 45% of the petroleum used in 2011. There is street roller on road with electric generator which get rotates while vehicle runs on road speed breaker to provide electricity via transmitter circuit. There is Sensor based Street light system to provide electric light whenever vehicle crosses that street at night time. Thus there are three functions as follow-Electric roller system on street road to provide electricity to vehicle and street light system. Wireless charging to electrical vehicle running on Smart Road. There is transmitter circuit smart road for electricity transfer to electric vehicle. It has oscillator circuit oscillates at 10MHz and with the help of transmitting coil it transmits the wireless power. Electric Vehicle has receiver circuit which receives the power through receiving coil and passes through voltage multiplier circuit (consisting of diode and capacitor voltage multiplier) and this multiplied voltage is used to charge their battery. Sensor based Street light System to provide facility on our traffic light at night time. As our road networks become more crowded, the use of street light is expanding, both to improve traffic flow, and to protect local environments from increased traffic exposure. Our Sensor based Street light system prevents wastage of electricity uses and provides advance lighting facility
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S, Shahanas. "Smart Parking Your Parking Assistant." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46338.
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Abstract—Smart Park It is a software solution designed to enhance the parking experience in locations with a high volume of vehicles, such as airports, shopping malls, and other busy venues. Upon entering a mall, drivers must find a parking space for their motorcycles or cars, often relying on security personnel to assist in locating an available slot. Retrieving a vehicle from a crowded parking area can be challenging, as it requires remembering the exact location of the parked vehicle. While this task may be manageable in smaller facilities, it becomes increasingly difficult in larger environments like international airports. To address this issue, we propose a software application that utilizes AI technology. Upon arrival at the parking facility, the vehicle's license plate will be scanned by AI cameras at the entrance, and a ticket featuring slot address and a QR code will be issued. Scanning this QR code will provide directions to the designated parking space, facilitating both the parking and retrieval processes. The system will require two cameras: one at the entrance to capture the license plate and another within the parking area to identify available slots. Introduction
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V., Geetha, Gomathy C.K., Pavan Manikanta Kiran Maddu, and Rajesh Gandikota. "The Implementation of Fleet Monitoring System using Mobile based Software." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 2264–68. https://doi.org/10.35940/ijeat.D9033.049420.
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Fleet management is an emerging area in analysis and development nowadays. Large scale Companies, emergency forces must to keep track of their trucks and cars and finding out where the vehicle is at the current timing. Our system includes two mobile applications one for organization head i.e., Smart Fleet and other for the driver of the vehicle (smart driver). This system enables the organization to keep track of their vehicles all the time. This is done by the geolocation of the mobile phone in which smart driver application is installed is shared to the mobile phone in which smart fleet application is installed. Organization head who is having our application installed is able to monitor the vehicles, updating the data.
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Zhang, Cong, Peiqian Liu, and Shan Ao. "Vehicle reputation value management scheme based on data uplink rules." Journal of Computing and Electronic Information Management 16, no. 3 (2025): 96–104. https://doi.org/10.54097/94c6gm03.
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Aiming at the security problems caused by the long-standing lack of trust between vehicles in the Internet of vehicles, a vehicle reputation-value management scheme based on data uplink rules is designed. value management scheme based on data uplink rules is designed. First, users release specific tasks according to their needs. First, users release specific tasks according to their needs. The blockchain generates smart contracts and conducts broadcast transactions within the scope of RSU to find vehicles to complete the tasks. The blockchain generates smart contracts and conducts broadcast transactions within the scope of RSU to find vehicles to complete the tasks. Finally, the credibility of the vehicle is comprehensively evaluated through multi-type vehicle weight, peripheral recommendations, rewards and punishment meschanisms, and the smart contract updates the reputation value of the vehicle in real time. Experiments show that the application of reputation management scheme in the Internet of vehicles is necessary, and the information trust evaluation and reputation integral algorithm are effective. Experiments show that the application of reputation management scheme in the Internet of vehicles is necessary, and the information trust evaluation and reputation integral algorithm are effective.
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Savale, Prof Vaishali. "IoT based Smart Parking System." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 11–13. http://dx.doi.org/10.22214/ijraset.2023.52579.
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Abstract: The number of vehicles keeps increasing each passing day and due to the limited parking space, parking has been a real nuisance. Our project which is based on the technology of “Internet of things”, aims to make parking vehicle easy and efficient. The benefit of our approach is that it allows us to find a parking space in parking area efficiently. Our project can also collect useful data like the amount of vehicle in the parking area, peak parking time etc. Parking will be digitalized and human efforts can be greatly reduced.
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Ismail, Ahsiah, Amelia Ritahani Ismail, Nur Azri Shaharuddin, et al. "Vision-Based Vehicle Classification for Smart City." Aptisi Transactions on Technopreneurship (ATT) 7, no. 2 (2025): 441–53. https://doi.org/10.34306/att.v7i2.446.
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Vehicle detection systems are essential for improving traffic management, enhancing safety, supporting law enforcement, facilitating toll collection, and contributing to smart city initiatives through real-time monitoring and data analysis. With the rapid growth of smart city technologies, the need for efficient, scalable, and high-accuracy vehicle detection models has become increasingly critical. This study aims to propose an advanced vehicle detection system using Convolutional Neural Networks (CNNs) in combination with the YOLOv5 model, which is known for its high-speed performance and superior accuracy in image recognition tasks. The proposed model is evaluated using a custom-trained YOLOv5s model, tested on a dataset comprising 1460 images of vehicles. These images are divided into five classes which are cars, motorcycles, trucks, ambulances, and buses. Performance evaluation metrics such as precision, recall, and mean Average Precision (mAP50-95) are used to assess the model's effectiveness. The results indicate that the YOLOv5-based model achieved impressive detection accuracy, with precision, recall, and mAP values exceeding 87%. The proposed system demonstrates its robustness in detecting and classifying various vehicle types across different conditions, including small, partially visible, and distant vehicles. The findings suggest that this model holds significant potential for real-world applications in urban traffic management and smart city infrastructure.
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Parwateeswar Gollapalli, Neha Muthyala, Prashanth Godugu, Nikitha Didikadi, and Pavan Kumar Ankem. "Speed sense: Smart traffic analysis with deep learning and machine learning." World Journal of Advanced Research and Reviews 21, no. 3 (2023): 2240–47. http://dx.doi.org/10.30574/wjarr.2024.21.3.0896.
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The main reason for many road accidents in modern times is speeding and negligent driving. This project aims to identify vehicles that exceed the speed limit and employs a machine learning algorithm for this purpose. It eliminates the need for manual checks by the police to identify speeding vehicles. The project involves vehicle detection and tracking as key steps, enabling us to classify the type of vehicle, their respective speeds, and the count of vehicles passing through given region. Counting the number of vehicles helps manage traffic, allowing us to identify peak traffic times and take necessary precautions to avoid long traffic jams. Vehicle tracking is the process of detecting a moving vehicle using a camera. Capture vehicle in video sequence from surveillance camera is demanding application to improve tracking performance. This technology is increasing the number of applications such as traffic control, traffic monitoring, traffic flow etc. Video and image processing are vital for traffic surveillance, analyzing, and monitoring in urban areas. Recent speed estimation methods prioritize accuracy and cost-effective hardware implementation.
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Parwateeswar, Gollapalli, Muthyala Neha, Godugu Prashanth, Didikadi Nikitha, and Kumar Ankem Pavan. "Speed sense: Smart traffic analysis with deep learning and machine learning." World Journal of Advanced Research and Reviews 21, no. 3 (2024): 2240–47. https://doi.org/10.5281/zenodo.14176162.
Full textAbstract:
The main reason for many road accidents in modern times is speeding and negligent driving. This project aims to identify vehicles that exceed the speed limit and employs a machine learning algorithm for this purpose. It eliminates the need for manual checks by the police to identify speeding vehicles. The project involves vehicle detection and tracking as key steps, enabling us to classify the type of vehicle, their respective speeds, and the count of vehicles passing through given region. Counting the number of vehicles helps manage traffic, allowing us to identify peak traffic times and take necessary precautions to avoid long traffic jams. Vehicle tracking is the process of detecting a moving vehicle using a camera. Capture vehicle in video sequence from surveillance camera is demanding application to improve tracking performance. This technology is increasing the number of applications such as traffic control, traffic monitoring, traffic flow etc. Video and image processing are vital for traffic surveillance, analyzing, and monitoring in urban areas. Recent speed estimation methods prioritize accuracy and cost-effective hardware implementation.
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Niture, Deeplaxmi V., Vivekanand Dhakane, Piyush Jawalkar, and Ankit Bamnote. "Smart Transportation System using IOT." International Journal of Engineering and Advanced Technology 10, no. 5 (2021): 434–38. http://dx.doi.org/10.35940/ijeat.e2870.0610521.
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In this paper a Smart Vehicle Assistance and Monitoring system (SVAMS) is presented. SVAMS is an intelligent transportation system (ITS), developed to tackle various traffic related issues. It is a traffic management, monitoring and optimization solution in which all the vehicles are interconnected through Zigbee and are monitored and assisted centrally, by a data center. The system has two parts; one part is mounted in/on the vehicle and the other part is at the data centre. Part one collects data from various sensors and transmits it to central data centre. All the data will be stored on cloud for further analysis, processing and future use. SVAMS is relatively low-cost, compact and has various functionalities such as emergency response, pollution level monitoring, automatic toll collection, traffic rule violation detection, vehicle tracking, etc. The use of SVAMS will help to build up Clean, Corruption free and Crime free (C-3) cities.
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Deeplaxmi, V. Niture, Dhakane Vivekanand, Jawalkar Piyush, and Bamnote Ankit. "Smart Transportation System using IOT." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 5 (2021): 434–38. https://doi.org/10.35940/ijeat.E2870.0610521.
Full textAbstract:
In this paper a Smart Vehicle Assistance and Monitoring system (SVAMS) is presented. SVAMS is an intelligent transportation system (ITS), developed to tackle various traffic related issues. It is a traffic management, monitoring and optimization solution in which all the vehicles are interconnected through Zigbee and are monitored and assisted centrally, by a data center. The system has two parts; one part is mounted in/on the vehicle and the other part is at the data centre. Part one collects data from various sensors and transmits it to central data centre. All the data will be stored on cloud for further analysis, processing and future use. SVAMS is relatively low-cost, compact and has various functionalities such as emergency response, pollution level monitoring, automatic toll collection, traffic rule violation detection, vehicle tracking, etc. The use of SVAMS will help to build up Clean, Corruption free and Crime free (C-3) cities.
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Barth, Matthew, Michael Todd, and Susan Shaheen. "Intelligent Transportation Technology Elements and Operational Methodologies for Shared-Use Vehicle Systems." Transportation Research Record: Journal of the Transportation Research Board 1841, no. 1 (2003): 99–108. http://dx.doi.org/10.3141/1841-11.
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There has been significant interest and activity in shared-use vehicle systems as an innovative mobility solution. Shared-use vehicle systems, that is, carsharing and station cars, consist of a fleet of vehicles used by several different individuals throughout the day. Shared-use vehicles offer the convenience of a private automobile and more flexibility than public transportation alone. From the 1990s to today, varying degrees of intelligent transportation system technologies have been applied to shared-used systems, providing better manageability and customer service. Many shared-use vehicle service providers today include some degree of advanced technologies (online reservations, vehicle tracking, smart card access) in their operations. Currently, there is a developing need for interoperability among shared-use vehicle service providers (e.g., smart card access among carsharing organizations) and transit operators (e.g., transit fare collection via smart cards). Interoperability will likely result in higher customer satisfaction and use, leading to greater market penetration. Similarly, some standardization will likely unfold for overall operational techniques (online reservations and insurance policies), customer interactions, and to some degree vehicle interfaces. Because shared-use vehicles systems are still a relatively new mobility concept, an industrywide standardization approach is still premature. Nevertheless, there are attempts to identify many of the important issues that will play a significant role in interoperability discussions among shared-use vehicle providers and the development of industry standards in the future. There are key elements in intelligent shared-use vehicle system operations and trade-offs encountered during the pioneering stage of shared-use vehicle system developments. Topics to discuss include vehicles, user–system interactions, user–vehicle interactions, and system operations.
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Souissi, Ahmed Saad Eddine, Habib Kraiem, Aymen Flah, and Amjad El Madani. "Improving Electric Vehicle Autonomy in the Smart City Concept." Engineering, Technology & Applied Science Research 14, no. 2 (2024): 13299–304. http://dx.doi.org/10.48084/etasr.6941.
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Organizing automobiles in a city is challenging due to the sensitive data that need to be disclosed. Information that can be utilized to identify a car and provide some useful characteristics about it is among the large amount of data that can be collected from an automobile. This operation will be easier if the vehicles are placed on a specific platform based on the smart city concept. Even if sensors and cameras are installed around the roads and the city, having the vehicle information will be more useful. The current study tries to demonstrate how it is feasible to improve vehicle autonomy by initially enhancing the vehicle's energetic performance, based on the smart city idea. Intelligent control topology serves as the foundation for the exposed energy management protocol. The suggested concept is created and the associated results are displayed using the Matlab Simulink platform.
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Andrian, Taufan Zandy, and Uke Kurniawan Usman. "Vehicle Monitoring with Web-Based Applications Utilizing High-Precision Positioning." International Conference on Information Science and Technology Innovation (ICoSTEC) 2, no. 1 (2023): 134–37. http://dx.doi.org/10.35842/icostec.v2i1.51.
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Motorized vehicles have facilitated various human activities in everyday life. As technology develops, various features in driving have been added to the vehicle itself. Starting with the aim of adding comfort, even to increase the safety in the use of vehicles. In the current digital era, the vehicle monitoring process has been widely applied, especially in a number of smart vehicles that are integrated with a monitoring system from various vendors. A web-based application or commonly known as webapp, designed by PT. Telkom Indonesia, Tbk. named IndiCar, is an example of a monitoring system, which can be applied to vehicles that are commonly used daily. This is in line with the main goal of IndiCar, which is the digitalization of public vehicles, into smart vehicles. The implementation of monitoring using the IndiCar webapp can make it easier for vehicle users, both in personal and institutional use, to track and diagnose a vehicle, regarding a number of factors such as precise location, track traveled, to the condition of various vehicle components, utilizing a number of sensors applied to the IndiCar system.
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Babar, Prof Madhuri, Sanket Meshram, Dhanashri Gulhane, Jagdish Kore, Harshad Nikhare, and Aditi Salodkar. "ANTI-THEFT ALERT SYSTEM FOR SMART VEHICLES USING MOBILE APP." International Journal of Engineering Applied Sciences and Technology 6, no. 6 (2021): 330–35. http://dx.doi.org/10.33564/ijeast.2021.v06i06.048.
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Theft security of vehicle in common parking places has become a matter to concern. To overcome this problem, we have an idea. There is a system that doesn’t allow tampering with ignition systems of vehicles, it captures the picture of a thief and also shares the location. The system will warn the mobile-phone about the tampering of the vehicles ignition key knob etc. when the parking mode is ON in our app. There is mobile application for warning the owner/mobile-phone. If the vehicle is lifted during parking mode, the app will alert the owner/mobile-phone through the app and also track the vehicle. In 2nd part of system, there is a Feature when an Unknown person allows to tamper with the Vehicle then Pi camera takes a picture and sending to registered application Gmail. There are the functions like this Tampering detection, tampering of ignition wire, attempt to lift, thief attempt to start vehicle without key, App/mobile warning, GPS tracking of vehicle, capturing picture of thief, and extra function apart from mobile application is Image processing system