Vehicle Location Research Articles

Segmentation Of Vehicle Delivery Test Services Using K-Means Clustering Case Study Of West Pasaman District Transportation Services

This research aims to segment services using the K-Means Clustering method in the case of Vehicle KIR Test services. Service segmentation is important to understand the time, place and type of vehicle to be tested, so that the West Pasaman Regency Transportation Service can direct service efforts more effectively. The K-Means Clustering method is used to group customers based on certain attributes, such as test location, test time, vehicle type and test status. KIR test service data is collected, including variables such as test location, test time, test status and type of vehicle to be tested. Research data was analyzed using K-Means Clustering analysis. The research results are categorized into 3 clusters, namely: 1. First cluster: KIR services with work as a trader, income around Rp. 3,473,882, located in the Ranah Batahan District area, the average visit time is 13.18 WIB which is also influenced by the distance traveled by KIR officers from Simpang Empat to Ranah Batahan ±82.3Km, 2. Second Cluster: KIR services with traders earning Rp. . 2,606,324, with the aim of KIR services to Koto Balingka District with a distance of ±56.3 Km and an average visiting hour of 10.37 WIB, the type of vehicle that is often tested is goods cars.3. Third Cluster: KIR services with self-employed work earning Rp. 4,544,071, with the aim of the KIR service to Pasaman District and the average visiting time is 11.05 WIB, the type of vehicle that is often tested is the trailer/outboard carriage.

Methods and Challenges of Cryptography-Based Privacy-Protection Algorithms for Vehicular Networks

With the rapid development of wireless communication technology, positioning technology, and modern smart devices, Internet of Vehicles (IoVs) smart vehicles have brought great convenience to human production and life. Meanwhile, privacy and security issues are becoming extremely serious, with serious consequences if sensitive data such as vehicle location and trip patterns are leaked. This paper focuses on the demands for vehicular network security, especially privacy protection and existing privacy-protection techniques, including common cryptography methods and cryptography-based advanced technologies. At the same time, this paper also analyzes the advantages and challenges of these technologies in protecting privacy and network security in the Internet of Vehicles, such as the challenges of computational resource requirements and security efficiency in the implementation process, as well as the complexity of realizing effective privacy protection in the interactions among different entities. Finally, this paper envisions the development of privacy-preserving application scenarios and the prospects for crypotography-based privacy-preserving technologies.

Daily motor vehicle traffic volume and other risk factors associated with road deaths in U.S. counties

Introduction: Road death risk is often characterized as deaths per volume of traffic in geographic regions, the denominator in miles or kilometers supposedly indicative of the magnitude of risk exposure. This paper reports an examination of the differences in the predictive value of factors hypothesized to influence traffic volume and road death risk. Method: The association of 11 risk factors in U.S. counties during the first 7 months of 2020 was examined for consistency of predictions of road death and traffic volume measured by cell phone and vehicle location data. The study employed least squares regression for traffic volume and Poisson regression for deaths with the population as the offset variable. Results: The directions of the regression coefficients for traffic volume and odds of road deaths per population were opposite from one another for 9 of the 11 variables in the analysis of vehicle occupant deaths. Only the coefficients for maximum daily temperature and Saturday travel were in the same direction. The confidence intervals of three risk ratios for pedestrian deaths indicated low reliability but most of the predictor variables were opposite in association with traffic volume and odds of death. Although traffic volume plunged in the first weeks of the pandemic, the results for the months before and during the COVID-19 pandemic were similar. Practical applications: Traffic volume is an inverse risk factor for road deaths at the local level, likely the result of lower speeds on congested roads. Without the application of countermeasures aimed at reducing speed and other risk factors, the reduction of road congestion is likely to increase deaths.

Experimental Evaluation of the Effectiveness of Using Visual Cues for Controlling Unmanned Vehicles

Purpose. The study aims to experimentally evaluate the effectiveness of using visual cues, namely ARToolKitPlus, ArUco markers and two-dimensional QR (quick response code) codes in the tasks of localizing unmanned vehicles (UVs) indoors. Methodology. To enable the implementation of the processes of localization and perception of unmanned vehicles based on visual marks (markers), the structure of the visual marks processing subsystem has been developed. An algorithm for the combined use of three types of visual markers – ARToolKitPlus, ArUco markers and QR codes – for localizing unmanned vehicles and identifying cargo is proposed using the example of an online store warehouse scenario. To conduct experiments on the markers, we chose a hardware and software tool such as the JeVois-A33 smart machine vision camera with the JeVois Markers Combo software module and the JeVois Inventor graphical interface. Findings. An experimental study of the possibility of correct recognition of visual marks for indoor working conditions was carried out. As a result of a series of experiments, the possibilities of correct recognition of visual marks such as ArUco, ARToolKitPlus markers and QR codes during scanning at right angles to the camera at a distance ranging from 0.3 to 2 meters were determined. Originality. The study obtained the probabilities of correct recognition of ArUco, ARToolKitPlus markers and two-dimensional QR codes in the conditions of localization of unmanned vehicles indoors. Practical value. The obtained results of the study can be used to create, simulate, and analyze the effectiveness of algorithms for localizing and perceiving unmanned vehicles indoors using appropriate visual markers. The proposed generalized structure of the visual marker processing subsystem of the localization and perception system can be used in the development of unmanned vehicle control systems.

An Underwater Passive Electric Field Positioning Method Based on Scalar Potential

In order to fulfill the practical application demands of precisely localizing underwater vehicles using passive electric field localization technology, we propose a scalar-potential-based method for the passive electric field localization of underwater vehicles. This method is grounded on an intelligent differential evolution algorithm and is particularly suited for use in three-layer and stratified oceanic environments. Firstly, based on the potential distribution law of constant current elements in a three-layer parallel stratified ocean environment, the mathematical positioning model is established using the mirror method. Secondly, the differential evolution (DE) algorithm is enhanced with a parameter-adaptive strategy and a boundary mutation processing mechanism to optimize the key objective function in the positioning problem. Additionally, the simulation experiments of the current element in the layered model prove the effectiveness of the proposed positioning method and show that it has no special requirements for the sensor measurement array, but the large range and moderate number of sensors are beneficial to improve the positioning effect. Finally, the laboratory experiments on the positioning method proposed in this paper, involving underwater simulated current elements and underwater vehicle tracks, were carried out successfully. The results indicate that the positioning method proposed in this paper can achieve the performance requirements of independent initial value, strong anti-noise capabilities, rapid positioning speed, easy implementation, and suitability in shallow sea environments. These findings suggest a promising practical application potential for the proposed method.

Enhanced Berth Mapping and Clothoid Trajectory Prediction Aided Intelligent Underground Localization

In response to the widespread absence of global navigation satellite system (GNSS) signals in underground parking scenes, we propose a multimodal localization method that integrates enhanced berth mapping with Clothoid trajectory prediction, enabling high-precision localization for intelligent vehicles in underground parking environments. This method began by constructing a lightweight map based on the key berths. The map consisted of a series of discrete nodes, each encompassing three elements: holistic and local scene features extracted from an around-view image, and the global pose of the mapping vehicle calculated using the positions of the key berth’s corner points. An adaptive localization strategy was employed during the localization phase based on the trajectory prediction result. A progressive localization strategy, relying on multi-scale feature matching, was applied to the nodes within the map coverage range. Additionally, a compensation localization strategy that combined odometry with the prior pose was utilized for the nodes outside the map coverage range. The experiments conducted in two typical underground parking scenes demonstrated that the proposed method achieved a trajectory prediction accuracy of 40 cm, a nearest map search accuracy exceeding 92%, and a metric localization accuracy meeting the 30 cm standard. These results indicate that the proposed approach satisfies the high-precision, robust, real-time localization requirements for intelligent vehicles in underground parking scenes, while effectively reducing the map memory requirements.

Graph-Based vs. Error State Kalman Filter-Based Fusion of 5G and Inertial Data for MAV Indoor Pose Estimation

5G New Radio Time of Arrival (ToA) data has the potential to revolutionize indoor localization for micro aerial vehicles (MAVs). However, its performance under varying network setups, especially when combined with IMU data for real-time localization, has not been fully explored so far. In this study, we develop an Error State Kalman Filter (ESKF) and a Pose Graph Optimization (PGO) approach to address this gap. We systematically evaluate the performance of the derived approaches for real-time MAV localization in realistic scenarios with 5G base stations in Line-Of-Sight (LOS), demonstrating the potential of 5G technologies in this domain. In order to experimentally test and compare our localization approaches, we augment the EuRoC MAV benchmark dataset for visual-inertial odometry with simulated yet highly realistic 5G ToA measurements. Our experimental results comprehensively assess the impact of varying network setups, including varying base station numbers and network configurations, on ToA-based MAV localization performance. The findings show promising results for seamless and robust localization using 5G ToA measurements, achieving an accuracy of 15 cm throughout the entire trajectory within a graph-based framework with five 5G base stations, and an accuracy of up to 34 cm in the case of ESKF-based localization. Additionally, we measure the run time of both algorithms and show that they are both fast enough for real-time implementation.

Facilitate Driving with Geographical Proximity

Abstract: This research paper describes the development of a website based on remote computing with the aim of improving the connection between driving schools and automobile students. The primary purpose of this web application is to provide administrators, parents, and students with actual vehicle information, including clutch usage, speed, braking patterns, and vehicle location. This is achieved through the integration of remote data processing installed in the vehicle and sending the relevant data to web applications. This article discusses the importance of these systems in improving driver training and safety by allowing participants to remotely monitor vehicle operation, driving behavior and actions. We completed the literature review to examine existing telematics systems and their applications in the automotive industry. The work includes the selection and installation of remote data processing equipment, the development of user- friendly web interfaces using modern programming languages and methods, and scrupulous compliance with technical procedures. The main features of the web application include real-time data tracking, GPS tracking, important event alerts and notifications, and reporting and analysis capability. The implementation process is quite detailed, including hardware integration, front-end and back-end development, data storage and security measures.

ARDUINO BASED ALCOHOL SENSE ENGINE LOCK USING GPS & GSM

This project introduces an innovative approach to improving road safety by developing an Arduino-based alcohol detection and engine lock system, integrated with GPS and GSM technologies. The main goal is to prevent driving under the influence of alcohol by disabling the vehicle's engine upon alcohol detection. The system employs an MQ-3 alcohol sensor to measure the driver's breath alcohol content. When alcohol is detected, the Arduino microcontroller processes the data from the sensor and activates the engine lock mechanism, thus preventing the vehicle from starting. This functionality is enhanced with GPS technology to provide precise location tracking of the vehicle. In the event of alcohol detection, the system utilizes the GSM module to send alerts to pre-configured contacts, supplying real-time information about the driver's condition and the vehicle's location. By incorporating GPS, the system ensures continuous location monitoring, adding an extra layer of security and enabling swift emergency response. The GSM module allows for immediate communication, dispatching SMS alerts to family members or authorities, thereby increasing the safety of both the driver and the public. This comprehensive solution aims to reduce incidents of drunk driving by leveraging Arduino for sensor interfacing and control, and combining it with GPS and GSM technologies for real-time tracking and communication. The proposed system is cost-effective, easy to implement, and scalable, offering a practical tool for enhancing road safety and saving lives. Key Words: Arduino Alcohol sensor, Engine lock, MQ- 3 sensor, GPS tracking, GSM module etc.

Map-based localization for intelligent vehicles based on fusion of multiple visual features in underground parking

The ‘last mile’ problem in intelligent car localization within underground parking lots presents challenges such as localization blind spots and feature extraction difficulties. To tackle these issues, this paper proposes a mapping and multiscale matching localization method based on the fusion of multiple feature sources using a ‘binocular mapping, monocular localization’ approach. By combining traditional features with deep learning features, the proposed method enhances feature matching accuracy and addresses feature extraction challenges. A sparse three-dimensional reconstruction method based on local feature-optical flow is also proposed to mitigate localization blind spots. During the mapping process, the underground parking lot scene is segmented into several nodes to store map information, each consisting of a feature layer, scene structure layer, and trajectory layer. In the multiscale matching localization process, ORB, LoFTR, and BoW multisource features are fused to achieve feature matching, determine the nearest node, and estimate vehicle pose using the PnP model through optical flow. Finally, a localization optimization algorithm outputs the localization result. Experiments were conducted across multiple underground parking lots encompassing a total area of over 100,000 square meters. The results demonstrated that the average error of map construction using the proposed algorithm is approximately 15 mm, and the average error rate of localization is 4.45%.

Construction of a Green and Low-Carbon Travel Order Prediction Model Based on Shared Bicycle Big Data

In the era of big data, traditional analysis methods are insufficient to meet the growing demand for green and low-carbon travel orders in shared bicycle systems. To address this issue, a new order demand forecasting model, named the “convolutional neural network (CNN)”—“long short-term memory (LSTM)” model (CNN-LSTM), is proposed by integrating CNN and LSTM techniques. The research further validates the spatiotemporal prediction performance of this model. The experimental results demonstrate that LSTM exhibits favorable prediction performance in terms of time feature analysis, as evidenced by the overlapping of the true value (TV) and predicted value (PV) curves. Notably, LSTM achieves an impressively low mean squared error (MSE) value of 0.0063, which is significantly lower compared to CNN (0.0082) and XGBoost (0.0074). Upon incorporating date and weather characteristics, the predictive performance improves significantly, achieving an outstanding MSE value of 0.0043. However, when it comes to spatial feature analysis, the LSTM algorithm alone proves inadequate, obtaining a MSE value of 0.0084. Thus, by employing the CNN-LSTM combination model, a lower MSE value of 0.0066 is achieved, outperforming the comparison algorithms. Overall, the CNN-LSTM model exhibits strong predictive capabilities regarding the temporal and spatial requirements of shared bicycles. This model plays a key role in accurately forecasting order demands, facilitating urban transportation planning and management, as well as guiding the planning and location of non-motorized vehicle stops.

Vehicle Tracking System Based on Internet of Things Utilizing TTGO T-CALL ESP32 SIM800l

Nowadays, cases of stolen vehicles are increasing rapidly, particularly in urban areas. The stolen vehicle is difficult for the owner to trace since the GPS system is rarely used due to its expensive cost. Thus, the purpose of this research is to analyze the accuracy of the real-time monitoring provided by the Blynk application using information obtained from GPS modules and to design a simple and compact smart vehicle monitoring anti-theft system utilizing the TTGO T-CALL ESP32 SIM800l with the Blynk application. Next, a tracking system is configured and developed by utilizing the Arduino IDE software to flash the code into the microcontroller and the Internet of Things platform as the connectivity to the Blynk App. Furthermore, the usability and functionality of the Global Positioning System (GPS) and TTGO T-CALL ESP32 SIM800l are essential in determining the vehicle's location. The use of TTGO T-CALL ESP32 SIM800l is to make the device more compact than previous research. The data has been obtained using a comparison of two microcontrollers; TTGO T-Call ESP32 SIM800l and the Arduino Mega connected to the GSM SIM800l. The data will be analyzed under two different parameters; the accuracy of altitude and speed. It can be seen that the TTGO T-Call ESP32 SIM800l has a lower percentage of latitude and longitude errors. It also has great performance in terms of speed accuracy, which is 88.5%.

Enhanced Unmanned Aerial Vehicle Localization in Dynamic Environments Using Monocular Simultaneous Localization and Mapping and Object Tracking

This work proposes an innovative approach to enhance the localization of unmanned aerial vehicles (UAVs) in dynamic environments. The methodology integrates a sophisticated object-tracking algorithm to augment the established simultaneous localization and mapping (ORB-SLAM) framework, utilizing only a monocular camera setup. Moving objects are detected by harnessing the power of YOLOv4, and a specialized Kalman filter is employed for tracking. The algorithm is integrated into the ORB-SLAM framework to improve UAV pose estimation by correcting the impact of moving elements and effectively removing features connected to dynamic elements from the ORB-SLAM process. Finally, the results obtained are recorded using the TUM RGB-D dataset. The results demonstrate that the proposed algorithm can effectively enhance the accuracy of pose estimation and exhibits high accuracy and robustness in real dynamic scenes.

Sound Source Localization for Unmanned Aerial Vehicles in Low Signal-to-Noise Ratio Environments

Sound Source Localization for Unmanned Aerial Vehicles in Low Signal-to-Noise Ratio Environments

Highly robust and accurate multi-sensor fusion localization system for complex and challenging scenarios

Highly robust and accurate localization using on-board sensors in the absence of GNSS is a key issue for long-term autonomous navigation of vehicles. However, the limited sensing capabilities of a single sensor cannot resist the effects of feature degradation and high-speed motion in complex and challenging scenarios, which results in the fragile of existing LiDAR-based and visual-based localization algorithms. To solve these problems, we propose a highly robust and accurate localization system by fusing information from multi-sensors. Firstly, we propose a global localization method that leverages deep learning models for processing visual information to provide an accurate initial position for vehicles, which solves the problem of global localization of vehicles in the absence of GNSS. Then, we propose a highly robust and accurate LiDAR-Vision-IMU localization method based on pose graph optimization, which integrates LiDAR-based poses and visual-inertial-based poses. The proposed method detects sensor degradation in real time to dynamically adjust the fusion weights, which solves the problem of low localization performance of a single sensor. Finally, we conduct experiments in complex and challenging scenarios, such as urban, tunnels, and unstructured mountains. Experimental results show that our method can achieve a global localization error of less than 0.5 m in complex scenes. In unstructured mountainous scenarios of length 21.1 km, the root mean square errors (RMSE) of translation and rotation are 0.67 m and 0.048rad, respectively. Compared to most existing algorithms, our method achieves the most robust and accurate localization performance.

Characterizing spatiotemporal patterns of bus bunching frequency on a bus route network: A case study of Taipei city

Bus bunching is a critical issue in bus operation management. This study investigated the impacts of potential factors affecting bus bunching on a city-wide bus route network, using Taipei City as an example with its 267 bus routes and 4741 bus stops. Frequency of bus bunching was measured on an hourly basis using Automatic Vehicle Location data. Multilevel count models based on Conway-Maxwell Poisson distributions were developed, and the spatiotemporal distributions of random intercepts and residuals were examined. The results showed that the effects of bus lane design, bus route network and operation, and traffic characteristics were consistent with those reported in prior studies. However, a quadratic relationship between bus route length, best measured by number of stops, and bus bunching was identified, which explains the conflicts between the theories of bus bunching propagation and self-repair. Bus route design often faces a dilemma between extending route length to collect additional passengers and reducing in-vehicle travel time to enhance service quality. The findings of the study could be a useful reference for bus route design to minimize bus bunching.

A Vision/Inertial Navigation/Global Navigation Satellite Integrated System for Relative and Absolute Localization in Land Vehicles.

This paper presents an enhanced ground vehicle localization method designed to address the challenges associated with state estimation for autonomous vehicles operating in diverse environments. The focus is specifically on the precise localization of position and orientation in both local and global coordinate systems. The proposed approach integrates local estimates generated by existing visual-inertial odometry (VIO) methods into global position information obtained from the Global Navigation Satellite System (GNSS). This integration is achieved through optimizing fusion in a pose graph, ensuring precise local estimation and drift-free global position estimation. Considering the inherent complexities in autonomous driving scenarios, such as the potential failures of a visual-inertial navigation system (VINS) and restrictions on GNSS signals in urban canyons, leading to disruptions in localization outcomes, we introduce an adaptive fusion mechanism. This mechanism allows seamless switching between three modes: utilizing only VINS, using only GNSS, and normal fusion. The effectiveness of the proposed algorithm is demonstrated through rigorous testing in the Carla simulation environment and challenging UrbanNav scenarios. The evaluation includes both qualitative and quantitative analyses, revealing that the method exhibits robustness and accuracy.

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

A group key agreement protocol for Vanet based on Chinese remainder theorem and blockchain

AbstractVehicular ad hoc Network (VANET) is a wireless self‐organizing network for obtaining information about road conditions, vehicle speed, vehicle location and traffic congestion. Traditional key negotiation protocols create many problems when dealing with a group of vehicles that need to communicate over a public channel. For example, traditional key negotiation protocols rely too much on the participation of trusted institutions and suffer from a single point of failure. Meanwhile, group session key negotiation is usually inefficient with high computational cost and communication overhead. To solve these problems, this paper proposes a blockchain‐based Chinese Remainder Theorem (CRT) VANET group key agreement. In addition, the protocol supports dynamic management of vehicles, including joining and exit. Formal security proofs show that our solution satisfies basic security requirements. Experiments using ProVerif show that the protocol functions properly even under many active and passive attacks, such as eavesdropping attacks. Performance analysis shows that the protocol is more efficient in the face of multi‐vehicle communication, in particular by reducing the computational cost by up to 75% and the communication overhead by up to 66%.

Optimal Location and Pricing of Electric Vehicle Charging Stations Using Machine Learning and Stackelberg Game

Optimal Location and Pricing of Electric Vehicle Charging Stations Using Machine Learning and Stackelberg Game