Vehicle Monitoring System Research Articles

Smart Crosswalk: Machine Learning and Image Processing based Pedestrian and Vehicle Monitoring System

The conventional pedestrian crossing system's shortcomings require urgent reform to enhance the safety of pedestrians and improve urban mobility. Issues such as insufficient time for pedestrians to cross, prolong waiting times, neglection of emergency vehicles, and the absence of effective 24/7 response mechanisms at traditional crosswalks present significant safety concerns in urban areas. Our primary intention is to develop a cutting-edge pedestrian crossing system that relies on deep learning and image processing technologies as its foundation. This research addresses to innovate an advanced smart crosswalk consisting of four essential components: a real-time Pedestrian Detection and Priority System customized for individuals with special needs, a responsive system for detecting road conditions, vehicle availability and speed near crosswalks, a real-time Emergency Vehicle Detection and Priority System strengthened by rigorous verification procedures, and a robust framework for identifying pedestrian accidents and violations of crosswalk rules. The entire system has been meticulously designed not only to enhance pedestrian safety by identifying potential dangers but also to optimize traffic flow. In essence, it aims to provide an improved pedestrian crossing experience characterized by increased safety and efficiency.

Smart evaluation of videos for unusual-event identification in automated vehicle monitoring systems

Many methods have been tried to perfect vehicle recognition, which includes quickly locating the same objects in cameras with different sensor types and perhaps propagating trustworthy information across cameras to improve detection. In this study, we look at various important issues and provide a new method for spotting certain abnormalities. We suggest an automated vehicle monitoring system (AVMS) to enhance detection accuracy. If the current reading from a certain local monitor is out of the ordinary, that monitor will issue a notification, and all of those alerts will go toward deciding whether or not an abnormal event has occurred. The effective implementation of any large-scale surveillance system relies on our algorithm meeting several parameters. It simply takes a few minutes to set up and is completely hands-free. The experimental findings demonstrate the effectiveness and simplicity of the suggested approach, with the assembled image quality being on the level with that of state-of-the-art techniques.

Remote Vehicle Monitoring System Starting and Tracking Using IoT System

Safety in the vehicle is one of the most important factors in the daily life of every vehicle user. This project is more focused on improving vehicle systems to increase vehicle safety systems. The main objective of this project is to study IoT (Internet of Things) safety systems in automotive applications. Next is to design engine start-stop and tracking for motorcycles. In addition, to analyze battery voltage, GPS location data, and engine start-stop system from the motorcycles with safety features. The vehicle used for this project is a Yamaha 135LC version 2 motorcycle, the Blynk app, and a two NodeMCU ESP8266 microcontroller with a Wi-Fi module, a relay, and a GPS module. As a result of the start-stop engine, PicoScope was used to analyze the voltage of the battery after the system had fully functioned when the signal was sent to the relay by Blynk apps. The experiment was carried out from five different locations for the engine start-stop system and seven different locations for the GPS, with the engine start-stop system obtaining motor battery voltage readings using PicoScope and the GPS obtaining vehicle coordinate results by reading the latitude and longitude from the smartphone. The GPS data was then compared between the location of the GPS module and the location of the smartphone's GPS to determine the accuracy of the location.

Machine learning-based soft-sensor development for road quality classification

Vibrations in road vehicles cause several harmful effects, health problems can occur for the passengers, and mechanical damage can occur to the vehicle components. Given the health, safety, and financial issues that arise, keeping the road network in good condition and detecting road defects as early as possible requires an extensive monitoring system. Related to this, our study presents the development of hardware and software for a low-cost, multi-sensor road quality monitoring system for passenger vehicles. The developed monitoring system can classify road sections according to their quality parameters into four classes. In order to detect vibrations in the vehicle, accelerometers and gyroscope sensors are installed at several points. Then, a machine learning-based soft-sensor development is introduced. Besides noise filtering, each data point is resampled by spatial frequency to reduce the velocity dependence. Subsequently, a decision tree-based classification model is trained using features from the power spectrum and principal component analysis. The classification algorithm is validated and tested with measurement data in a real-world environment. In addition to reviewing the accuracy of the model, we examine the correlation of the data measured in the cabin and on the suspension to see how much additional information is provided by the sensor on the axle.

Smart Borewell Vehicle Monitoring System: A Microcontroller-based GSM Informer

Abstract: This research paper presents an automatic borewell vehicle status informer designed using the Arduino MEGA 2560 microcontroller. The aim of this study is to enhance monitoring and communication in borewell operations, addressing the concerns of vehicle owners and customers. The system utilizes proximity sensors to calculate the number of borewell rods drilled and unloaded, while the location of the vehicle is tracked using GPS. Compressor runtime is recorded through an RTC or GPS module. Real-time SMS notifications are sent to the owner and customer when the compressor is switched on, providing location and time details. After completion of drilling, SMS messages containing the number of rods drilled and total compressor runtime are sent using a GSM module. All functionalities are controlled by the Arduino MEGA 2560 microcontroller, with measured parameters stored on an SD card. The proposed microcontroller-based informer integrates sensors and modules to improve efficiency and transparency in borewell operations. By providing accurate information and real-time communication, this system aims to enhance customer satisfaction and optimize the utilization of borewell resources.

"IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management"

Curbing road accidents havealways been one of the utmost priority of nations worldwide. In Malaysia, the Traffic Investigation and Enforcement Department reported that Malaysia’s total number of road accidents haveincreased from 373,071 to 533,875 in the last decade. One of the significant causes of road accidentsis the driver’s behaviors. However, to regulate drivers’ behavior by the enforcement team or fleet operatorsischallenging, especially for heavy vehicles. In our research, we have proposed the Internet of Things (IoT) scalability framework and its’ emerging technologies to monitor and alert driver’s behavioral and driving patterns to reduceroad accidents. To prove this work, we have implementeda lane tracking,and iris detection algorithm, to monitor and alert the driver’s behavior when the vehicle sways away from the lane, and to detect if the driver is feeling drowsy. We implemented electronic devices such as cameras, a global positioning system module, a global system communication module, and a microcontroller as the hardware for an intelligent system in the vehicle. We also appliedface recognition for person identification using the same in-vehicle camera and recorded the working duration for authentication and operation health monitoring. With the GPS module, we monitored and alerted against permissible vehicle’s speed accordingly. We integrated IoT on the system for the fleet centre to monitor and alert the driver’s behavioral activities in real-time through the user access portal. We have validated it successfully on Malaysian roads. The outcome of this pilot project ensuresthe safety of drivers, public road users, and passengers. The impact of this framework leads to a new regulation by the government agencies towards merit and demerit system, real-time fleet monitoring of intelligent transportation systems, and socio-economy such as cheaper health premiums. The big data can be used to predict the driver’s behavioral in the future.

Oгляд інформаційних та комунікаційних технологій і систем моніторингу на транспорті та аналіз їхніх можливостей для формування і впровадження інноваційних технологій технічного обслуговування і ремонту транспортних засобів

Preventive maintenance is performed at fixed intervals based on pre-made statistical estimates of when a failure may occur or when a part may need to be replaced. Predictive maintenance is performed at unfixed intervals that are determined based on predictive estimates of when a failure may occur or when a part may need to be replaced. The predictive maintenance system is based on the automated collection and automated processing of all technical information necessary to predict the most optimal intervals. This possibility is provided by innovative information technologies for monitoring vehicles connected to the internet. An overview of the possibilities of infor-mation and communication technologies and monitoring systems in road transport, in particular, technologies connected to the Internet vehicles, which allow developing and implementing innovative maintenance and repair technologies, was conducted. The possibilities of connecting “unconnected” cars to the Internet are considered and given. The potential possibilities of a gradual transition from a planned and preventive maintenance system to a “predictive” system, which is based on the constant monitoring of the processes of changing diagnostic parameters and related parameters characterizing the modes and conditions of operation of the engine, transmission and the vehicle as a whole, are analyzed. A review of the current state of development, implementation and application of service systems “on operational need” and forecasting processes of changing the technical condition and forecasting the possibility of malfunctions for their prevention in motor vehicle enterprises was carried out. The main conclusions are drawn based on the analysis of informational materials and work experience of such companies as Stratio Automotive, Pitstop Connect, Web Eye, as well as BOSCH, LAUNCH, and Electronic Team Inc. Keywords: innovative technology, information technology, vehicle monitoring system, connected vehicle, remote diagnosis, real-time monitoring of diagnostic parameters, preventive maintenance system, predictive maintenance system, prediction of changes in technical condition, automated system maintenance planning, vehicle location monitoring.

Heterogeneous Blockchain-Based Secure Framework for UAV Data

Unmanned aerial vehicles, drones, and internet of things (IoT) based devices have acquired significant traction due to their enhanced usefulness. The primary use is aerial surveying of restricted or inaccessible locations. Based on the aforementioned aspects, the current study provides a method based on blockchain technology for ensuring the safety and confidentiality of data collected by virtual circuit-based devices. To test the efficacy of the suggested technique, an IoT-based application is integrated with a simulated vehicle monitoring system. Pentatope-based elliptic curve encryption and secure hash algorithm (SHA) are employed to provide anonymity in data storage. The cloud platform stores technical information, authentication, integrity, and vehicular responses. Additionally, the Ethbalance MetaMask wallet is used for BCN-based transactions. Conspicuously, the suggested technique aids in the prevention of several attacks, including plaintext attacks and ciphertext attacks, on sensitive information. When compared to the state-of-the-art techniques, the outcomes demonstrate the effectiveness and safety of the suggested method in terms of operational cost (2.95 units), scalability (14.98 units), reliability (96.07%), and stability (0.82).

Construction of Battery Health Monitoring System for New Energy Vehicles from a Multi-dimensional Perspective

With the continuous development of the national economy, new energy vehicles, as eco-friendly and low-carbon transportation tools, are gradually promoted by various countries and societies. Electric vehicles account for the highest proportion of new energy vehicles. Therefore, this study analyzed the battery health monitoring of new energy vehicles. By building a relevant evaluation index system, the paper quantified the battery health status to obtain the healthy life of the battery through the evaluation method. Experimental analysis shows that the method proposed in this study has a certain accuracy and can provide auxiliary decision support for relevant departments.

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors.

This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver's health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle's central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver's heart condition and vehicular traffic have been found in this analysis.

SMALL ROAD MONITORING SYSTEM FOR FOUR WHEEL VEHICLES

The application of the vehicle regulation system at intersections in rural areas does not yet have a good regulatory system compared to the vehicle control system in urban areas. The lack of equitable distribution of road widening in rural areas makes people often have problems accessing roads when using four-wheeled vehicles. People often experience problems when accessing small roads, especially when more than one vehicle passes at the same time. This problem was also experienced in DesaTanjung Sari, Kecamatan Cijeruk, Kabupaten Bogor. This study aims to design and build a small road monitoring system so that car drivers get a warning if there are four-wheeled vehicles that have passed the small road. The object recognition method used in the small road monitoring system for four-wheeled vehicles is the HC-SR04 ultrasonic sensor which is placed on the left, right, and top sides. Arduino Uno R3 microcontroller will be used as the main system device. The communication between points used is LoRa Ra-02 as the sender and receiver. The results of this study show the results of the system prototype running well in accordance with the planned functions.

Analysis of Automobile Wheel Counting using Novel adaboosting Algorithm with Accuracy Compared to Logistic Regression Algorithm

Aim: In order to determine the accuracy of a realtime traffic management system, this work compares novel adaboosting and logistic regression methods to forecast the AutoMobile Wheel Movement Counting. Materials and Methods: The dataset utilized in this article contains 12 columns or attributes and a total of 10,684 rows. The columns in the dataset are named Car Wheels, Bicycle Wheels, Motorcycle Wheels, and Truck Wheels. The data source link provided a sample size of 1,340 records. A Novel adaboosting algorithm (N=20) and Logistic regression (N=20) iterations are simulated by various parameters and automate vehicle monitoring systems to optimize the pH. The 40 iterations were calculated using CilnCal with G power 80% and CI of 95%. Results: Based on obtained results Novel adaboosting Algorithm has significantly better accuracy (84.71%) compared to Logistic regression Algorithm accuracy (80.60%). Statistical significance difference between Novel adaboosting and Logistic regression algorithm was found to be p=0.013 (Independent Sample T Test p<0.05). Conclusion: Novel adaboosting algorithms provide better results in Finding Road Traffic counting than Logistic regression algorithms.

Automated Vehicle Monitoring Framework based on Machine Learning Architecture for People with Cardiovascular Diseases

This research develops an Automated Vehicle Monitoring system with the use of an Integrated Machine Learning architecture so that it can cater to the requirements of people who have Cardiovascular Diseases (CVD). For the purpose of this research, the participants’ heart rates and pulses were monitored by a machine learning system as they drove an automobile. A simulation has been developed in order to evaluate the performance of the machine learning classifier when applied to data taken from the actual world, such as the heart rate of a patient. The findings of the simulation indicate that the suggested technique is more accurate than the approaches that have been used in the past.

Leveraging Azure Data Lake for Efficient Data Processing in Telematics

In the telematics industry, the continuous generation of large volumes of data presents significant challenges in terms of storage, processing, and analysis. Azure Data Lake, a scalable and secure data storage solution, offers an efficient platform to handle these massive datasets. This paper explores the application of Azure Data Lake in telematics for efficient data processing, focusing on its capacity to store vast amounts of structured and unstructured data while providing seamless integration with various analytics tools. By leveraging Azure Data Lake, organizations can enhance their data processing capabilities, improve decision-making, and reduce operational costs. The study investigates how Azure Data Lake simplifies data management through its high availability and accessibility, allowing businesses to manage data from multiple sources with minimal complexity. Furthermore, the integration with Azure services like Azure Data Factory and Azure Databricks facilitates advanced analytics, enabling real-time insights and predictive analytics, which are crucial for the telematics sector. The findings suggest that adopting Azure Data Lake improves data processing efficiency, enhances scalability, and supports the development of innovative telematics applications such as fleet management and vehicle monitoring systems. The paper concludes by highlighting the potential of Azure Data Lake to revolutionize the telematics industry by enabling more agile and data-driven operations.

A Monitoring System for Electric Vehicle Charging Stations: A Prototype in the Amazon

Among the main problems faced in the context of electric mobility today, the management and monitoring of electric vehicle charging stations, the integration between the diverse types of technologies that make up its architecture, and its low scalability stand out. Therefore, we will present the implementation and complete integration of an electric vehicle charging system in an electric mobility pilot project being executed in the Amazon region in Brazil. Therefore, a literature review of related works will be presented, and its entire implementation will be addressed, from the charging infrastructure, through its back-end system and its Internet of things platform, to its front-end web system for monitoring charging stations. In addition, a complete prototype is created with a real testbed to verify the scalability of the implemented physical system. Based on the testbed evaluations performed, we observe that the implemented system performs well in receiving and sending data from up to 160 electric vehicle charging stations, achieving an average consumption of 26% for CPU and 95% for memory. In addition, it is important to mention that the deployed system supports horizontal scalability, enabling the connection of more charging stations and making it ideal for other integrated systems similar to ours. Based upon the main results obtained with the implemented system, the possibility of carrying out the management and monitoring of charging stations stands out; the integration of different technologies, from the back end and IoT middleware to its front end; a system that supports scalability, enabling the connection of more charging stations; and a reference architecture for charging station management and monitoring systems for the Amazon region.

Active Safety System in Vehicles using Sensor Network Model

The imperative need to enforce safety measures to prevent deadly accidents exists in the world of urban traffic environments today. During 2021, two-wheelers have been reckoned for the maximum number of fatal roads accidents accounting for 69,240 fatalities, or 44.5 of total road accidental deaths, followed by cars (23,531 deaths, 15.1) and trucks/lorries (14,622 deaths, 9.4%). Accidents may happen regularly as a result of drivers' carelessness and imperfect indicators of faulty warning signs on the road. Road safety policy's primary focus for several decades included techniques like signs, speed limits, and straightening bends. The system provides the Global Positioning System (GPS), Geographic Information System (GIS), and Global System for Mobile Communications (GSM) to cater to the requirements of some intelligent vehicle monitoring systems. The research presented in this study sheds light on the vehicle's decision-making process by using a novel approach.

Forecasting fuel consumption in means of transport with the use of machine learning

Transport is a key factor influencing greenhouse gas emissions. In relation to this, the issues and challenges facing the transport industry were presented. The issues of challenges for the transport industry related to the European Green Deal were discussed. It discussed how the transport system is critical for European companies and global supply chains. The issues related to the exposure of society to costs are presented: greenhouse gas emissions and pollution. The article deals with the issues of managing transport processes in an enterprise. It was decided to raise the topic of fuel consumption in means of transport. Based on a review of the scientific literature, 3 categories of features are indicated: the vehicle characteristics, the driver's characteristics, and the route's impact on fuel consumption. The study is based on actual data from the archives of the GPS vehicle monitoring system. Data was collected on 1890 routes operated between May 30, 2020, and May 31, 2021. The routes were performed by twenty-nine drivers and 8 vehicles. The vehicles are 40-ton road sets consisting of a tractor unit and a semi-trailer. The analysis of factors influencing fuel consumption is presented. The methodology for conducting feature engineering is described. The benefits of using the method of reducing fuel consumption are presented. The possibilities of using the methods of forecasting electricity and hydrogen consumption in various means of transport, including public transport, where indicated. The data is processed using the Pandas library. The models are compared according to the MAE success measure. The application of methods of working with large data sets is presented. The calculations are made with the help of the NumPy library. Data visualization is done with Matplotlib and Seaborn. Scikit-Learn models are used.

Active Drag Reduction for the Wall of Microunderwater Vehicles by Piezoelectric Actuated Drag Reducer

The driving performance and endurance mileage of microunderwater vehicle can be improved by reducing the wall friction drag. The existing underwater drag reducers can achieve good drag reduction performance, but they are not suitable for microunderwater vehicle due to the large structure size and the high energy consumption. An active drag reducer, which is driven by piezoelectric actuator and can be embedded into the outer wall of microunderwater vehicle hardly changing the streamline structure and structural strength of the wall, is proposed. The active drag reduction on demand and effective energy saving can be realized by combining with the motion monitoring system of the underwater vehicle. The drag reduction mechanism of the proposed drag reducer driven by piezoelectric actuator is discussed. The influences of single parameter and comprehensive effect of parameters on the characteristics of the near-wall turbulent boundary layer are obtained, and the control methods of drag reduction performance for meeting different requirements are proposed. The simulation and experimental results show that the proposed microdrag reducer can effectively reduce the frictional and total drag, and can achieve positive energy saving. The drag reduction rate of 37.82% can be achieved with the excitation amplitude of 150 V and the operating frequency of 1500 Hz. This article can provide an important reference for the research of active drag reduction of microunderwater vehicle.

An Indirect State-of-Health Estimation Method Based on Improved Genetic and Back Propagation for Online Lithium-Ion Battery Used in Electric Vehicles

Lithium-ion battery state of health (SOH) estimation technology is an important part of the design of a battery monitoring system (BMS) for electric vehicles. People often use battery capacity and internal resistance as SOH estimation indicators. However, due to actual working conditions, it is difficult for electric vehicles to achieve complete charge and discharge, so the battery capacity and internal resistance cannot be monitored online. In view of the above questions, this article proposes an indirect SOH estimation method for online EV lithium-ion batteries based on arctangent function adaptive genetic algorithm combination with back propagation neural network (ATAGA-BP). Firstly, constant current drop time (CCDT), constant current drop capacity (CCDC) and maximum constant current drop rate (MCCDR) in constant voltage charging stage are used as health indicator (HI) to evaluate battery SOH in order to indirectly quantify the degradation process of lithium-ion batteries. Error point optimization and correlation verification are also carried out. Secondly, an ATAGA-BP algorithm is proposed to establish the relationship between HI and available battery capacity, and SOH estimate is made for lithium-ion batteries according to the proposed algorithm. Finally, simulation results with NASA data show the correlation between the proposed HI and lithium-ion battery capacity is above 85%, the error of SOH estimation method proposed is 3.7%, and the iteration efficiency is increased by 17.8%.

IoT-BASED VEHICLE TRACKING SYSTEM FOR KHULNA UNIVERSITY

Right now, there is no perceptible vehicle management framework for Khulna university that provides a vehicle monitoring system, real-time fuel consumption data analysis, vehicle route optimization by calculating passenger traffic, and so on. Albeit creating that framework is the holistic objective of the total project, the significant objective of this paper is to propose a cost-effective, reliable IoT-based vehicle monitoring system for Khulna University based on the developed prototype device. The open-source controller and GPS-GPRS-based module determine the real-time position of the vehicle, and the location of the test vehicle can be communicated via GPRS, which is provided by the GSM network. This real-time location data is stored in a web-based IoT platform. Authorized users of the system can access this information via the internet. The proposed solution will facilitate all stakeholders, including teachers, students, and other Khulna University staffs, to use this information to make smarter travel selections. This will also pave the way for future research like intelligent vehicle route optimization by storing real data from different vehicles of Khulna University on the online database.