Intelligent Vehicle Management Research Articles

Above the roads: Unleashing unmanned aerial vehicles and image processing for traffic analysis

Road traffic surveillance using unmanned aerial vehicles is a practice that can be found especially in the field of intelligent vehicle management, which is still in the early stages of research and application. This paper presents three methods of analyzing traffic data. One method is a conventional one, based on Doppler radar detection and the other two methods analyze images captured by unmanned aerial vehicles, being based on deep learning techniques. After acquiring the images, they went through a complex processing process to eliminate noise and improve the clarity of the image, then the identification of the vehicles was done by recognizing moving objects and highlighting them either through a bounding box or through labelling. The quality of images obtained from unmanned aerial vehicles is similar to the quality of images obtained from fixed surveillance cameras. The comparative analysis of the results obtained through image processing, together with those obtained through a conventional method of traffic analysis, the Doppler radar, highlighted the fact that video detection used in intelligent vehicle management is a method that both researchers and local authorities can rely on the performance of traffic studies or the analysis of traffic incidents and accidents.

Data-driven approaches to achieve energy transition: Statistical analysis of demand-side management and smart decision making in urban energy hub networks

Energy hubs (EHs) have emerged as a crucial solution to address resource scarcity and environmental concerns by integrating various technologies for energy production, conversion, and storage. These hubs encompass diverse elements such as integrated heat, electricity, and cooling systems, renewable energy sources, batteries, and thermal energy storage units (TES). This study focuses on a residential EH model that receives inputs in the form of electricity, gas, and solar energy to satisfy the household's electricity, heat, and cooling requirements. To enhance the operational flexibility of this energy hub, the study employs a Demand Response Program (DRP), which facilitates load shifting, load curtailment, and the modeling of flexible heating loads. The primary objective is to develop an efficient heat and power management system that optimally schedules household devices, generation equipment, and storage units. In pursuit of this goal, Long Short-Term Memory (LSTM) techniques have been selected for renewable energy, market price, and load forecasting, enhancing the system's realism and predictive accuracy. The Wild Goats algorithm is employed to solve an optimization problem for three distinct scenarios, aiming to minimize overall power costs while maintaining user comfort levels with regard to temperature and hot water availability. Furthermore, this study incorporates an environmentally conscious approach by considering the reduction of CO2, SO2, and NO pollution originating from consumers in a multi-objective optimization framework. The findings reveal that the implementation of DRP, intelligent Plug-in Hybrid Electric Vehicle (PHEV) management, TES technologies, and LSTM-based forecasting significantly contribute to reducing power costs within the proposed EH scheme. This research underscores the potential of data-driven strategies in advancing energy transition initiatives and fostering sustainable urban energy hubs while acknowledging the role of advanced forecasting techniques in enhancing system realism and efficiency.

Intelligent Vehicle Management: Harnessing Emerging Technologies for Efficiency and Sustainability

Vehicle Management System provides useful features for the users (drivers) to make drive easy and use application smoothly without any friction. In today’s fast-growing digital world of internet, we see often see many conflicts and disputes based on buying and selling old vehicle cause by the lack of transparency. Also, there is huge traffic in metro cities and not able to find parking areas and get fined. So, people are not able to find the available area and if available only some types vehicles can be parked like bikes and cars. There are such more problem related vehicles that exists in real world like connecting with mechanic for breakdown of vehicle. To Solve some of these problems we are intended to make a Vehicle system that helps an drive to make drive easy and reduce dispute.

Improved CNN-based CatBoost model for license plate remote sensing image classification

The rapid development of artificial intelligence technology has brought profound technical changes to many traditional industries, among which intelligent transportation has become a hot spot for development in the traditional transportation field. Remote sensing license plate image recognition technology is extensively emphasized in such domains as intelligent transportation and intelligent vehicle management. However, in the practical traffic environment, low visibility scenes caused by complex environmental factors such as rain, snow, haze and cloudy days influence the recognition and classification of license plates, while the distortion of license plate images that may be caused by irregular movements of vehicles bring challenges to license plate recognition classification. The CNN-CatBoost model proposed in this paper divides the license plate recognition classification into two stages. The first stage uses the excellent performance of convolutional neural network in processing image data to extract various license plate image features; the second stage uses the CatBoost module to further process the image feature data and finally obtain the remote sensing license plate information. The model achieves outstanding results in the experiments and has practical application value. Through comparison with other network models, the CNN-CatBoost model proposed in this paper has superior performance.

Guest Editorial Artificial Intelligence and Deep Learning for Intelligent and Sustainable Traffic and Vehicle Management (VANETs)

Intelligence and sustainability are two essential drivers for the development of current and future Intelligent Transportation Systems. On one hand, the complexity of vehicular ecosystems and the inherently risk-prone circumstances under which pedestrian and vehicles coexist call for the endowment of intelligent functionalities in almost all systems and processes participating in such ecosystems. On the other hand, risk may be the most important objective to be guaranteed by the provision of intelligence in ITS, but it is not certainly the only one: when safety is assured, sustainability comes into play, seeking to convey intelligence to the distinct parts composing the ITS landscape with efficiency, minimum carbon footprint, wastage of resources or any other factor affected by the technological empowerment itself.

An intelligent green vehicle management system for urban food reliably delivery:A case study of Shanghai, China

Efficient and trustable food delivery systems have a great impact on our daily life. Considering environmental protection, Electric Cold-chain Vehicle (ECV) has been adopted to support the food delivery system in urban area. However, the drivers of ECV has the motivation to power off the refrigerator for saving electricity, which would lead to the food safety issue. Driven by this, we proposed a novel blockchain-based Electric Cold-chain Delivery System (BECDS) and concentrated on the EV charging and scheduling problem in the distribution network. In terms of methodology innovation, the Dynamic charging and service schedule model (DCSSM) is proposed based on the multi-agent-based simulation. Meanwhile, Internet of Things (IOT) devices are used to monitor the environment data of food, which would be recorded in the blockchain. Furthermore, this paper carries out numerical experiments to verify the feasibility of this system via realistic data. Interestingly, the result shows that the new cold-chain delivery system could serve retailers more efficiently with zero emissions. Hence, this paper could broaden and defined a promising green and digital urban food delivery prototype in the future.

Dataset: Traffic Images Captured from UAVs for Use in Training Machine Vision Algorithms for Traffic Management

A dataset of Spanish road traffic images taken from unmanned aerial vehicles (UAV) is presented with the purpose of being used to train artificial vision algorithms, among which those based on convolutional neural networks stand out. This article explains the process of creating the complete dataset, which involves the acquisition of the data and images, the labeling of the vehicles, anonymization, data validation by training a simple neural network model, and the description of the structure and contents of the dataset (which amounts to 15,070 images). The images were captured by drones (but would be similar to those that could be obtained by fixed cameras) in the field of intelligent vehicle management. The presented dataset is available and accessible to improve the performance of road traffic vision and management systems since there is a lack of resources in this specific domain.

Simulation Study on an ICT-Based Maritime Management and Safety Framework for Movable Bridges

Maritime management is a crucial concern for movable bridge safety. Irregular management of water vehicles near movable bridges may lead to collision among ships and bridge infrastructures, causing massive losses of life and property. The paper presents a theoretical framework and simulation of an intelligent water vehicle management system for movable bridges corresponding to vehicle traffic responses. The water regime around the bridge is considered in virtually separated domains to estimate the desired safety actions based on the position of the approaching ships. An emergency clash avoidance control system is represented to prevent ship-infrastructure collision and ensure transportation safety. In addition, a simulation platform is developed specifically adaptable for movable bridge maritime and dynamic traffic management. The proposed theory is experimented using the simulation platform for different ship speeds and bridge-vehicle traffic volumes. Based on analyzing the velocity profile of approaching ships at different incidents, the bridge is found incapable of evacuating vehicles and unable to open promptly in case of speeding ships and high traffic density of vehicles on the bridge. Computational results show that the emergency control system is effective in reducing ship speed and prevent certain collisions. Lastly, the transportation policy for the newly proposed maritime management system is validated by real-world implementation in movable bridges across the world.

Architecture and Key Technologies of Mine Internet of Vehicles

As the important equipment of mine production, the level of mine vehicles management has a direct impact on all aspects of production, safety and management of mine. The application of IoV(internet of vehicles) to improve the intelligent level of mine vehicle management is an important part of intelligent mine construction. In order to avoid the problems of scattered development, repeated construction and difficult system integration caused by unclear concept, different objectives, different paths, different system architecture and different technical application emphases in the application of IoV in the mine vehicle management, taking the intelligent coal mine as an example, the concept of MIoV (mine internet of vehicles) is proposed, and its system architecture, key technologies and typical applications are analyzed and discussed. Firstly, on the basis of reviewing the development of intelligent mine and IoV, the concept of MIoV is defined and expounded; secondly, referring to the architecture of IoV and MIoT (mine internet of things), the architecture of MIoV is designed, which is composed of sense and control layer, network layer, service support layer and application layer; the key technologies of MIoV and intelligent mine information system are integrated and analyzed key technologies such as positioning and monitoring, intelligent perception and control, wireless communication, collaborative computing. Finally, from the four aspects of human-vehicle-environment-management, mine vehicle and driver monitoring, mine vehicle automatic driving and driverless, mine safety monitoring based on mine vehicles, mine vehicle intelligent scheduling and other typical application of MIoV are discussed. The research results laid a foundation for the construction of MIoV to promote the in-depth and orderly application of IoV in intelligent mine vehicle management, enrich the technical connotation of MIoT, and accumulate experience for the application of other technology branches of MIoT in intelligent mine construction.

Demystifying the Crowd Intelligence in Last Mile Parcel Delivery for Smart Cities

Recent years have witnessed an explosive growth of online shopping, which has posted unprecedented pressure on the logistics industry, especially the last mile parcel delivery. Existing solutions mostly rely on dedicated couriers, which suffer from high cost and low elasticity when dealing with a massive amount of local addresses. Advances in the Internet of Things, however, have enabled vehicle information to be readily accessible anytime anywhere, forming an Internet of Vehicles (IoV), which further enables intelligent vehicle scheduling and management. New opportunities therefore arise toward efficient and elastic last mile delivery for smart cities. In this article, we seek novel solutions to improve the last mile parcel delivery with crowd intelligence. We first review the existing and emerging solutions for last mile parcel delivery. We then discuss the advances of the ride-sharing- based delivery mechanism, identifying the unique opportunities and challenges therein. We further present Car4Pac, an IoV-enabled intelligent ride-sharing-based delivery system for smart cities, and demonstrate its superiority with real trace-driven evaluations.

Application of a Traffic Flow Prediction Model Based on Neural Network in Intelligent Vehicle Management

The ultimate direction of intelligent vehicle management is to achieve artificial intelligence (AI), and data mining is an important supporting technology for AI. The adoption of new AI technology can effectively improve operational efficiency and safety, especially in terms of performance. This paper takes the researches on traffic jam as an example and proposes one algorithm for combination forecasting model based on a segmentation algorithm for traffic flow sequence and BP neural network prediction. In this paper, it also introduces the traffic flow clustering analysis and mining algorithms for congestion events at the intersections. The blocking point algorithm is improved, and experimental analysis is performed through samples. Experimental results show that the algorithm use for combination forecasting model can greatly improve the real-time performance of short-term traffic flow prediction and significantly reduce the prediction error rate. Therefore, this algorithm has practical and innovative significance in the field of intelligent vehicle management.

RFID Assisted Intelligent Vehicle Communication Management System

RFID is an important technology in the Internet of things that has the characteristics of safe, affordable, efficient, which received widespread attention and research. This paper proposes a UHF RFID-based intelligent vehicle management system (the Internet). The system consists of RFID hardware system, CDMA system, the GIA system, data processing system, and can realize intelligent vehicle identification, location, tracking, velocity measurement, monitoring and management, to address the current severe road congestion, speeding, vehicle theft and vehicle overload management and other issues. Software and hardware design of intelligent vehicle management system based on RFID, focuses on systems, security design, selection of RFID cards and vehicle access control software features, the fleet management system is not only convenient, but also intelligent identification of vehicles and vehicle theft, in the community, school, car park and vehicle management occasions has a good application.

A Campus Intelligent Vehicle Management System Based on ETC Technology

A Campus Intelligent Vehicle Management System Based on ETC Technology

ETC technology and its Application in the Campus Intelligent Vehicle Management System

In order to solve the problem that the vehicles within the university campus are increasing steadily and a large number of outside vehicles drive through the campus arbitrarily and park disorderly. In this paper, a campus intelligent vehicle management system based on ETC is designed, and the ETC technology is introduced. On the basis of analyzing the system’s hardware architecture and workflow, a design model of lane is given. Finally, it discusses the communication theory of Winsock, customs a format of data frame and proposes a Winsock-based intelligent data synchronization algorithm. The daily traffic volume has been decreased by 65% since the system is put into operation in a university.

Local Internet Construction of Information System

in order to facilitate effective on LAN platform to realize intelligent district vehicle management systems, using RFID convenience, uniqueness for vehicle management of the community to provide convenience and security. Using RFID technology to establish district vehicle intelligent control system, realize the vehicle management of efficient, convenient and safe.

Study on RFID-Based Intelligent Management System for Vehicles

Along with the increase of the number of motor vehicles in urban areas, the relatively backward parking lot management has resulted in many problems such as the difficulty of parking and chaos management. In this paper, a RFID-based vehicle intelligent management system is designed, and its system structure, working flow and software design idea are also introduced in details. This intelligent vehicle management system is cored at ultrahigh RFID (radio frequency identification device), in which legal vehicles are unnecessary to stop in entering and exiting, and also all identification and judgment are automatically implemented by the system from a remote distance.

An intelligent vehicle management system for reducing greenhouse gas emission - a case study of hybrid vehicle engine

In this paper, a genetic algorithm based vehicle management system is proposed to determine ‘green’ components of a new vehicle engine so as to reduce the greenhouse gases emission level. The proposed system is developed using genetic algorithm (GA) to define the appropriate sets of components for the development of vehicle engine. The novel idea of this paper is to develop a system that considers the transport system infrastructure as one of the important factor for the design of vehicle engine system. The proposed system is applicable for the vehicle engine development into the current hybrid electric vehicle. A case study is conducted to illustrate the functionality of the proposed system, the scenario study and operations procedures of GA are also discussed in the case study. Finally, results from the simulation highlight the value of this system toward the reduction of the fuel consumption and pollutants.

Research on License Plate Recognition Technology

With the growth in number of vehicle, intelligent vehicle management has become the research hotspot, and license plate automatic recognition system is also of great concern as a important technology of intelligent traffic system. In this paper, Hough transform used to edge extraction, tilt correction algorithm and character recognition based on Haursdorff distance are discussed. At last, the license plate recognition system is designed and implemented, and then the experimental result is analyzed.

The TerraMax autonomous vehicle

AbstractThe TerraMax vehicle is based on Oshkosh Truck's Medium Tactical Vehicle Replacement truck platform and was one of the five vehicles able to successfully reach the finish line of the 132 miles DARPA Grand Challenge desert race. Due to its size (30 000 pounds, 27′ 0″ long, 8′ 4″ wide, and 8′ 2″ high) and the narrow passages, TerraMax had to travel slowly, but its capabilities demonstrated the maturity of the overall system. Rockwell Collins developed, integrated, and installed the intelligent Vehicle Management System, which includes vehicle sensor management, navigation, and vehicle control systems. The University of Parma provided the vehicle's vision system, while Oshkosh Truck Corp. provided project management, system integration, low level controls hardware, modeling and simulation support, and the vehicle. © 2006 Wiley Periodicals, Inc.