Vehicle Location Research Articles

Editorial for Metaverse (Volume 5, Issue 1)

Welcome to the latest issue of <em>Metaverse</em>, where we explore the intersection of technology, art, and society in this rapidly evolving digital landscape. This issue delves into diverse aspects of the metaverse, from the integration of AI in creative processes to advancements in autonomous vehicle localization, and from blockchain-based cybersecurity in mobile commerce to the transformative impact of digital strategies on emotional health.

Analyzing the impact of semantic LoD3 building models on image-based vehicle localization

Analyzing the impact of semantic LoD3 building models on image-based vehicle localization

An intelligent FL-based vehicle route optimization protocol for green and sustainable IoT connected IoV

The intelligent Internet of Vehicles (IoV) provides superior results in effectively addressing complex transportation challenges. Predicting vehicle traffic, crashes, demand, location, communication, and travel safety are all critical issues in today’s transportation systems. The proposed paper optimizes vehicle traffic by incorporating reroute recommendations, increasing the use of public transportation, and providing onboard vehicle drivers with intelligent health assistance using Federated Learning (FL). This research also focuses on resolving complex transportation issues such as a vehicle’s current location, exact vehicle count information on each route, and onboard vehicle vacant seat information. Furthermore, vehicle communication contributes to the proposed system’s efficiency by avoiding communication delays or information loss to registered users and the cloud server. An intelligent FL-based scheme for vehicle route optimization has been proposed as part of this research to prevent vehicle traffic in a real-time Internet of Things (IoT) connected IoV transportation system. The vehicle detection approach determines the number of vehicles traveling on each route to recommend the best route to registered users. The effective implementation of cluster-based vehicle communication and location estimation models enhances the efficiency of the proposed system.

Automatic lane change based on dynamic occupancy of an adaptive gird zone

This paper presents adaptive and occupancy grid map algorithms for automatic lane change technology, a core technology in autonomous vehicles. The objectives are to improve driver safety and convenience with technology that automatically changes lanes at the request of the driver. The algorithms construct nine grids on the basis of ego vehicles and generate adaptive and occupancy grid maps by using the relative speeds of ego and target vehicles. When a driver requests a lane change, the adaptive grid map reduces the number of cases where the target vehicles may exist around the ego vehicle from 256 to 32, thus decreasing the calculation amount. Therefore, the algorithms are suitable for use in autonomous vehicles that require real-time calculations. An occupancy grid map is formed in accordance with the location of the target vehicles, and whether lane changes are possible is determined. The algorithms generate a virtual simulation environment with the CarMaker and are simulated using Matlab/Simulink. An experiment is conducted in a real driving environment with real vehicles to prove the validity of the algorithms.© 2017 Elsevier Inc. All rights reserved.

An Integrated LSTM-Rule-Based Fusion Method for the Localization of Intelligent Vehicles in a Complex Environment.

To improve the accuracy and robustness of autonomous vehicle localization in a complex environment, this paper proposes a multi-source fusion localization method that integrates GPS, laser SLAM, and an odometer model. Firstly, fuzzy rules are constructed to accurately analyze the in-vehicle localization deviation and confidence factor to improve the initial fusion localization accuracy. Then, an odometer model for obtaining the projected localization trajectory is constructed. Considering the high accuracy of the odometer's projected trajectory within a short distance, we used the shape of the projected localization trajectory to inhibit the initial fusion localization noise and used trajectory matching to obtain an accurate localization. Finally, the Dual-LSTM network is constructed to predict the localization and build an electronic fence to guarantee the safety of the vehicle while also guaranteeing the updating of short-distance localization information of the vehicle when the above-mentioned fusion localization is unreliable. Under the limited arithmetic condition of the vehicle platform, accurate and reliable localization is realized in a complex environment. The proposed method was verified by long-time operation on the real vehicle platform, and compared with the EKF fusion localization method, the average root mean square error of localization was reduced by 66%, reaching centimeter-level localization accuracy.

Environmental information-assisted intelligent fusion localization for vehicles in urban area

In this paper, we propose a localization methodology aimed at improving accuracy through two primary aspects: environment information assisting fusion algorithm and the elaborate localization errors prediction. First, the environment-assisted particle filter (EA-PF) is proposed by enhancing the particle filter algorithm with perceived environmental feature information, which is reflected by the rasterized map constructed based on LiDAR sensors. Then, an elaborate localization error prediction model is designed to accurately forecast Inertial Navigation System (INS) errors. The model comprises the noise characteristic feature extraction and encoding network and multi-feature error prediction network. The localization errors in the EA-PF output can be further compensated. The proposed localization methodology can be divided into two operational modes based on the availability of Global Navigation Satellite System (GNSS) signal: GNSS-available and GNSS-unavailable modes. In the GNSS-available mode, the EA-PF algorithm yields precise localization results, while in the GNSS-unavailable modes, the elaborate localization error prediction model can effectively predict INS errors and provide the compensation for the EA-PF. To validate the effectiveness of the proposed methodology, relevant test experiments were performed using the KITTI dataset. The experimental validation results demonstrate the feasibility and effectiveness of the proposed methodology. The experimental results demonstrate that the EA-PF outperforms traditional PF methods by improving the root mean square error (RMS.) by 32.70 % on average during 90 s GNSS outages. Moreover, the proposed localization methodology achieves 2.30 m RMS error on average during 90 s GNSS outage.

MCG-SLAM: Tightly coupled SLAM for multi-factor constraint graph optimisation

Simultaneous localization and mapping (SLAM) technology is a core component for achieving high-precision vehicle localization and navigation in the field of autonomous driving. Existing light detection and ranging (LiDAR) SLAM methods typically neglect intersensory constraints when estimating poses based on sensor observations, resulting in reduced accuracy in multi-sensor fusion scenarios. To address this, a tightly coupled SLAM method with multi-factor constraint graph optimisation, referred to as MCG-SLAM, is proposed. The initial odometry information obtained from the extended Kalman filter is used to optimise the state nodes in the multi-factor constraint graph. This is achieved by dynamically adjusting the noise covariance of the odometry factors, global positioning system factors, and loop closure factors, optimising the factor nodes within the multi-factor constraint graph, and continuously updating the multi-factor constraint graph to complete pose optimisation. Furthermore, a loop closure detection method based on the intensity and geometric information of the point cloud is introduced to identify loop closures and incorporate loop closure factors. In this paper, the MCG-SLAM method is comprehensively validated against mainstream LiDAR SLAM methods using the MulRan dataset. The results show that MCG-SLAM outperforms other methods by improving LiDAR SLAM accuracy in localization and mapping.

Location-routing optimization problem for electric vehicle charging stations in an uncertain transportation network: An adaptive co-evolutionary clustering algorithm

The rapid growth of Electric Vehicles (EVs) has led to issues such as insufficient and unevenly distributed charging stations, posing an increasingly severe challenge. This not only means higher economic and time costs for EV users traveling to charging stations, but also indicates that EVs need to incur additional energy consumption. To address these challenges, this paper first establishes an extended space-time-state network to describe the temporal variability and uncertainty of the traffic environment. Secondly, considering the impact of the energy consumption incurred by EVs traveling to charging stations (CSs) on the layout of CSs, a co-evolutionary optimization model of the location-routing problem is proposed. To solve the model, a two-stage Adaptive Co-evolutionary Clustering Algorithm (ACECA) integrating an adaptive clustering framework and a co-evolutionary mechanism is designed. Finally, the experiment results indicate that there exists a game between a short-term economic investment and long-term benefits of energy conservation and emission reduction. Moreover, ACECA demonstrates stronger solution performance and robustness compared to other algorithms, with the resulting CS location schemes showing superior advantages in energy conservation and in cost saving for users. The research findings can provide theoretical support for the planning of charging station locations for electric vehicles.

Global Navigation Satellite System/Inertial Measurement Unit/Camera/HD Map Integrated Localization for Autonomous Vehicles in Challenging Urban Tunnel Scenarios

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs’ localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios.

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.