Navigation Applications Research Articles

Antecedents of Waze Mobile Application Usage as a Solution for Sustainable Traffic Management among Gen Z

The study aimed to investigate Gen Z’s intentions to utilize the Waze mobile application as a solution for sustainable traffic management in Metro Manila. The extended technology acceptance model (TAM) was used to define user behavior and technology usage to determine the factors influencing Filipino drivers’ acceptance of the Waze application. The study utilized a survey given to 300 Filipino drivers travelling within the National Capital Region to accurately measure various factors and their connections to usage intention and actual use. The analytical approach used for this investigation was partial least squares structural equation modelling (PLS-SEM). The study’s data results revealed that system quality, perceived location accuracy, perceived usefulness, and perceived ease of use affect Gen Z’s intentions to use traffic navigation applications, particularly Waze. The study’s results can be applied as a theoretical framework for future researchers of Waze mobile applications, Waze route accuracy, and Waze overall application performance, as well as the features of the Waze application, to evaluate how the use of the Waze mobile application could help in the effective management of traffic situations in Metro Manila. The study assessed the effectiveness of the Waze navigation app as a platform for efficiently managing traffic situations in Metro Manila and highlighted the application performance of Waze and its acceptance by Gen Z users in Metro Manila. The study also highlighted the need a critical review of Waze’s application performance. The study recommends that similar future studies must consider samples from a more diverse geographic backgrounds to obtain a more accurate representation of Filipino users. Future researchers are also advised to adopt a qualitative research design to gain more detailed insights into Filipinos regarding their experience with the Waze application.

Modeling and suppression of atomic comagnetometer’s response to micro-vibration

Alkali-metal-noble-gas comagnetometers have broad applications in fundamental physics research and inertial navigation. Micro-vibration, as one of the main disturbances of the system, significantly limits the sensitivity of the comagnetometer. However, there are no researches to systematically test and quantify the effect of micro-vibration on comagnetometer, and suppressing this effect remains a challenge. In this paper, the response characteristics of the comagnetometer to angular and linear vibration are analyzed and tested based on the designed experimental setup for the first time. And then, we analyze the response characteristics of comagnetometer with passive vibration isolation platform to the ground vibration noise and the effect of height of center of mass on vibration transfer. Based on the established model and test results, we optimized the structure, the center of mass, and the direction of the sensitive axis of the comagnetometer, and the vibration equivalent sensitivity of comagnetometer is suppressed by about 20 times in the range of 1–4.75 Hz.

Effects of Gamma Ray Radiation on the Performance of Microelectromechanical Resonators

While much radiation test data are available for metal‐oxide‐semiconductor (MOS) devices, research into the effects of radiation on microelectromechanical systems (MEMS) is in its relative infancy. Piezoelectrically transduced MEMS resonators have broad applications in signal processing, environmental monitoring, and navigation. Aluminum nitride (AlN), in particular, is an attractive piezoelectric because of its favorable fabrication characteristics and ease of integration into the complementary MOS (CMOS) manufacturing process. The utility of these devices in space and nuclear systems necessitates research into their performance in radiation environments. Resiliency and an established relationship between radiation dose and device behavior provide a critical tool for engineers in their design process. Multiple AlN‐based MEMS resonator designs are created and exposed the devices to 1 Mrad(Si) gamma irradiation from a Cobalt‐60 source while measuring scattering (S‐) parameters in situ. The experimental data are matched to a theoretical model to describe the change in frequency as a function of radiation‐induced displacement damage. It is demonstrated that the AlN‐based resonators are resilient against radiation‐induced charge‐trapping effects. Furthermore, a new method is presented of permanent frequency trimming MEMS resonators up to 30% of their bandwidth without modifying quality factor or motional resistance.

Improving Visual SLAM by Combining SVO and ORB-SLAM2 with a Complementary Filter to Enhance Indoor Mini-Drone Localization under Varying Conditions

Mini-drones can be used for a variety of tasks, ranging from weather monitoring to package delivery, search and rescue, and also recreation. In outdoor scenarios, they leverage Global Positioning Systems (GPS) and/or similar systems for localization in order to preserve safety and performance. In indoor scenarios, technologies such as Visual Simultaneous Localization and Mapping (V-SLAM) are used instead. However, more advancements are still required for mini-drone navigation applications, especially in the case of stricter safety requirements. In this research, a novel method for enhancing indoor mini-drone localization performance is proposed. By merging Oriented Rotated Brief SLAM (ORB-SLAM2) and Semi-Direct Monocular Visual Odometry (SVO) via an Adaptive Complementary Filter (ACF), the proposed strategy achieves better position estimates under various conditions (low light in low-surface-texture environments and high flying speed), showing an average percentage error of 18.1% and 25.9% smaller than that of ORB-SLAM and SVO against the ground-truth.

Hybrid NV Center Magnetometer With Directional Vector Resolution Based on Magnetic Flux Concentrators

Since the introduction of Hall magnetometers, different types of magnetometers have been widely used. Among them, magnetometers based on nitrogen-vacancy (NV) centers in diamonds are promising room-temperature solid-state sensors. Their sensitivity to low-frequency (< 10 Hz) magnetic fields has been reported to be currently at the nT level. Still, limitations in the detectable spatial range hinder their potential applications in medical imaging, earth sciences, and navigation. The most recent sensitivity enhancement technique involves using high-permeability flux concentrators with a unique magnetic field amplification structure that can cause the NV magnetometer to lose its vector detection capabilities. To address this limitation, we proposed a hybrid magnetometer based on a diamond-based hetero-NV center, which collects magnetic flux over a larger area and concentrates it in the diamond magnetometer, from which the magnetic field vector can be calculated by measuring the specific magnetic field direction vector. The low-frequency sensitivity of 90 pT∙Hz-1/2 under conventional ambient conditions has been solved by inserting NV-doped diamond blocks between four permalloy concentrators in a triangular structure, which solves the vector deficiency problem of traditional magnetic concentrators. High-sensitivity real-time variation acquisition of the magnetic field signal has been achieved by applying an AC magnetic field in combination with modulation and demodulation methods.

High-Frequency, Multiband Projector Using 1–3 Connectivity Lithium Niobate Composites With an Inversion Layer

In this study, we demonstrate a sound navigation and ranging (SONAR) projector with a higher and wider frequency range using an inversion layer. Because the working frequency range of the acoustic transducers is limited, several types of transducers are required to operate at different frequencies for underwater acoustic systems. Multiband acoustic transducers have been developed to solve this problem; however, the operating frequency range is only 100–200 kHz. To overcome this, we used lithium niobate (LN) to maximize the advantage of using an inversion layer. The characteristics of LN according to the angle of the cutting direction and the characteristics of 1–3 composites were calculated, and finite element method (FEM) simulations were performed using these calculated values. The simulated and measured results of the inversion layer composite and fabricated projectors showed similar electrical impedance responses and transmitting voltage response (TVR). Resonance of the inversion layer composite occurred at 330, 680, and 1100 kHz. The fabricated projector had resonance frequencies of 390 and 790 kHz, and the working frequency range of the fabricated projector was 270–900 kHz. We expect that the developed projector will exhibit excellent performance in underwater navigation applications, such as small-scale object detection and terrain mapping.

Broadband and high gain coupled and cascaded square ring antenna for RADAR applications

Gain and bandwidth are the two primary performance characteristics of an antenna that dictate its role and utilization. In this paper, the design and fabrication details of a coupled and cascaded square ring antenna for S-band (2–4 GHz) radar communications such as navigation applications are presented. The design is employed by two square coupled rings for bandwidth enhancement and two in-phase cascaded square rings for enrichment in gain. The fabricated antenna has delivered 33% (2.52–3.52 GHz) of 10-dB bandwidth and 11.2–13.1 dBi of broadside gain. The in-phase cascading ring technique improved the antenna's gain without increasing reflector size and ring height. Thus, the proposed design is compact, with a height of 0.2λ and a total planar area of 2λ2. Moreover, the performance of the antenna is presented for variations in the geometrical parameters also.

Traffic Safety Pyramid Based on Visual Navigation Distraction: A Naturalistic Study on App-Taxis

Evidence suggests that mobile phone navigation applications (MPNAs) may reduce road traffic safety by causing a visual distraction. This study aimed to (i) identify a list of road safety-related events (RSREs) that could occur while looking at the MPNA, (ii) examine the effect of app-taxFi drivers’ glances at MPNA on missing of RSREs, (iii) develop any potential new indicator in road traffic safety, and (iv) develop a pyramid of traffic incidents. This study investigated 36 app-taxi drivers in real road traffic circumstances. Data were collected by video recording using a double-lens camera. The events that occurred while the app-taxi driver was looking at the MPNA were extracted, recorded, and classified. A time span of less than one second and a new indicator, namely “seeing the event BEFORE visual distraction” (seBvd), were examined. The data were analyzed using descriptive statistics and non-parametric tests with a significance level of 0.05 in SPSS 18. A list of 23 events was identified. The most frequent event was “presence of pedestrians across the road” (including crossing the road and beside the road 36%). The number of glances at the MPNA had a significant correlation with time span ≤1 s ( p = 0.001, r &lt; 0.6). The total time of looking at the MPNA had a significant correlation with time span &gt;1 s ( p = 0.001, r &lt; 0.6). A pyramid of traffic incidents was developed based on the results. The new seBvd indicator is a binary (positive; negative) surrogate safety measure that can be useful in evaluating the effect of visual distraction on road traffic safety. The introduced pyramid of traffic incidents seems useful in traffic safety.

Multi-Sensor Data Fusion Solutions for Blind and Visually Impaired: Research and Commercial Navigation Applications for Indoor and Outdoor Spaces

Several assistive technology solutions, targeting the group of Blind and Visually Impaired (BVI), have been proposed in the literature utilizing multi-sensor data fusion techniques. Furthermore, several commercial systems are currently being used in real-life scenarios by BVI individuals. However, given the rate by which new publications are made, the available review studies become quickly outdated. Moreover, there is no comparative study regarding the multi-sensor data fusion techniques between those found in the research literature and those being used in the commercial applications that many BVI individuals trust to complete their everyday activities. The objective of this study is to classify the available multi-sensor data fusion solutions found in the research literature and the commercial applications, conduct a comparative study between the most popular commercial applications (Blindsquare, Lazarillo, Ariadne GPS, Nav by ViaOpta, Seeing Assistant Move) regarding the supported features as well as compare the two most popular ones (Blindsquare and Lazarillo) with the BlindRouteVision application, developed by the authors, from the standpoint of Usability and User Experience (UX) through field testing. The literature review of sensor-fusion solutions highlights the trends of utilizing computer vision and deep learning techniques, the comparison of the commercial applications reveals their features, strengths, and weaknesses while Usability and UX demonstrate that BVI individuals are willing to sacrifice a wealth of features for more reliable navigation.

Statistical modelling of digital elevation models for GNSS-based navigation

ABSTRACT Recently, smart mobility has become a important activity in transportation systems such as public, autonomous and shared transports. These systems require reliable navigation applications that lead to precise localisation and optimised route. The GPS system may face problems such as signal degradation caused by conical effects, affecting the reliability and accuracy of the signal, or signal loss in poor visibility environments. By using other sensors, the vehicle location system can overcome these GPS problems. This work focuses on the estimation of the inclination, which will be used to optimise the route planning for the EV or HEV especially in order to control the energy consumption. This paper presents a multi-sensor fusion method, based on GNSS, INS, OSM and DEM data fused using a non-linear particle filter, to estimate and improve the slopes of road segments. A new statistical modelling of the DEM errors related to the spatial sampling of elevation data is proposed. This method is based on the definition of a geometrical window, called Adjacent Sliding Window (ASW), which dynamically selects the elevation data in the vicinity of the road. The proposed method is evaluated in a suburban transport network. The experimental results show the benefits of the vehicle attitude and road slope estimation accuracies.

Robotic navigation during spine surgery: an update of literature

ABSTRACT Introduction The application of robotic navigation during spine surgery has advanced rapidly over the past two decades, especially in the last 5 years. Robotic systems in spine surgery may offer potential advantages for both patients and surgeons. This article serves as an update to our previous review and explores the current status of spine surgery robots in clinical settings. Areas covered We evaluated the literature published from 2020 to 2022 on the outcomes of robotics-assisted spine surgery, including accuracy and its influencing factors, radiation exposure, and follow-up results. Expert opinion The application of robotics in spine surgery has driven spine surgery into a new era of precision treatment through a form of artificial intelligence assistance that compensates for the limitations of human abilities. Modularized robot configurations, intelligent alignment and planning incorporating multimodal images, efficient and simple human – machine interaction, accurate surgical status monitoring, and safe control strategies are the main technical features for the development of orthopedic surgical robots. The use of robotics-assisted decompression, osteotomies, and decision-making warrants further study. Future investigations should focus on patients’ needs while continuing to explore in-depth medical – industrial collaborative development innovations that improve the overall utilization of artificial intelligence and sophistication in disease treatment.

Traffic Order Analysis of Intersection Entrance Based on Aggressive Driving Behavior Data Using CatBoost and SHAP

Analyzing road risks and developing targeted countermeasures are essential for a safe and orderly traffic flow. However, previous intersection safety analyses were conducted based on crash data. Little research has been conducted on surrogate safety measures based on risky driving behavior. In this study, categorical boosting (CatBoost) and Shapley additive explanation (SHAP) were used to analyze the impact of features on traffic order using a set of multisource data that include roadway geometry, signal control, and land use. The traffic data for intersection entrances in Beijing were collected from navigation systems, field investigations, and application programming interfaces. The model results showed that CatBoost exhibits a prediction accuracy of 83.5%, a recall of 83.5%, and an F1 score of 81.1%. Moreover, the importance, total effects, main effects, and interaction effects of influence factors were analyzed by using SHAP. It was found that the congestion index (CI) has significant negative effects on traffic order. A larger number of lanes and more electronic traffic control were found to have a positive effect on traffic order. Intersection entrances with three-phase signals or off-peak intersection entrances helped increase traffic order. Moreover, a high green ratio for through vehicles can reduce the positive impact of CI on traffic order when the value of CI is 1.1–1.4, and the signal control scheme with a high left-turn green ratio would result in a safe and orderly traffic flow. The results from this study can be used for further studies on improving traffic safety at signalized intersections.

Mobile Surveillance Siren Against Moving Object as a Support System for Blind People

Visually impaired people can use smartphone navigation applications to arrive at their destination. However, those applications do not provide the means to detect moving objects. This paper presents an Android application that uses the smartphone’s camera to provide real-time object detection. Images captured by the camera are to be processed digitally. The model then predicts objects from the processed image using a Convolutional Neural Network (CNN) stored in mobile devices. The model returns bounding boxes for each of the detected objects. These bounding boxes are used to calculate the distance from the object to the camera. The model used is SSD MobileNet V1, which is pre-trained using the Common Objects in Context (COCO) dataset. System testing is divided into object distance and accuracy testing. Results show that the margin of error for calculating distance is below 5% for distances under 8 meters. The mean average precision is 0.9393, while the mean average recall is 0.4479. It means that the system can recognize moving objects through the embedded model in a smartphone.

Temperature compensation of MEMS resonant accelerometers with an on-chip platinum film thermometer

Temperature-dominated drift is generally the main error source for high-performance micromachined resonant accelerometers (MRAs) due to inherent thermal stress effect of resonator structure and die-attach process. This paper describes the design and experimental evaluation of a temperature compensation scheme for MEMS resonant accelerometers that demonstrates excellent bias and scale factor stability against temperature variation. An on-chip temperature sensor fabricated by sputtering platinum film on glass substrate is proposed to accurately sense the temperature-induced frequency change of the resonator. A polynomial fitting-based post-compensation model is firstly used to suppress the temperature sensitivity of the MRA over dynamic temperature environment. The temperature drift test and compensation of four accelerometer prototypes in a range from −40 ∘C to 60 ∘C show that the stability of bias and scale factor has been improved greatly with navigation-grade performance. Temperature compensation results with three improved drift models based on polynomial fitting, convolutional neural network and support vector regression respectively are presented and compared to suppress the temperature drift hysteresis in consecutive temperature-varying tests. These experimental results indicate that this resonant accelerometer exhibits excellent temperature stability after compensation, which offers the promise for high-performance inertial navigation applications.

Near-infrared star map simulation for starlight refraction sensor based on ray tracing

The application of starlight refraction navigation to spacecraft and space weapons is a significant development direction. Due to the huge cost of spacecraft experiments, the functional validation of this technology relies mainly on ground simulations. The existing refraction star-map simulation ignores the starlight refraction angle’s change with time and position, which cannot meet the engineering requirements. Based on the timed/saber dataset and geometric ray theory, this paper presents a ray tracing algorithm to calculate the starlight refraction angle in different propagation paths. Combined with the coordinate conversion and energy transfer, a near-infrared refraction star-map simulation method is proposed. An all-time star sensor is used for simulation, which works in the 900–1700 nm band and can suppress strong limb background. The experimental data verify the refraction angle and energy model of the proposed method. Through the comparison between the measured data and simulated star maps, the starlight refraction angle’s bias error of the proposed method is −0.05″, which is typically smaller than −3.30″ of the traditional method, and the average flux error is 11%. The evaluation and verification outcome support the proposed star-map simulation method and can provide technical support for the optimization design, capability estimation, and algorithm validation of the starlight refraction navigation.

Research on a Horizon Line Detection Method for Unmanned Surface Vehicles in Complex Environments

A critical step in the visual navigation of unmanned surface vehicles (USVs) is horizon line detection, which can be used to adjust the altitude as well as for obstacle avoidance in complex environments. In this paper, a real-time and accurate detection method for the horizon line is proposed. Our approach first differentiates the complexity of navigational scenes using the angular second moment (ASM) parameters in the grey level co-occurrence matrix (GLCM). Then, the region of interest (ROI) is initially extracted using minimal human interaction for the complex navigation scenes, while subsequent frames are dynamically acquired using automatic feature point matching. The matched ROI can be maximally removed from the complex background, and the Zernike-moment-based edges are extracted from the obtained ROI. Finally, complete sea horizon information is obtained through a linear fitting of the lower edge points to the edge information. Through various experiments carried out on a classical dataset, our own datasets, and that of another previously published paper, we illustrate the significance and accuracy of this technique for various complex environments. The results show that the performance has potential applications for the autonomous navigation and control of USVs.

How to Reduce Traffic Jams when Flood Happens

Most map applications play the job of navigating users and informing them of traffic jams. However, they often cannot deal with an emergent situation when a disaster such as a flood takes place. This paper aims at providing a solution for map and navigation applications how to ensure users safety when natural disasters of floods happen. The subject uses sensors, programming, and perceptrons to prompt the study and take the Beijing-Hangzhou canal as a sample since each river has its warning water levels. Nevertheless, this paper provides a solution to the research aim, no experiments are involved.

DESIGN AND IMPLEMENTATION OF STEREO VISUAL ODOMETRY SYSTEM BASED ON ROP ADJUSTMENT

Abstract. Stereo Visual Odometry (SVO) is a technique used to estimate the continuous position and orientation of a moving platform using a dual-camera system that captures stereo image pairs. To obtain accurate results, A SVO system must be calibrated before use. System calibration is necessary to determine the intrinsic camera parameters (ICPs) and the relative orientation parameters (ROPs) between the cameras at real scale. Compared to monocular visual odometry, a calibrated SVO system can recover the real scale of the translation vector without additional sensors.The proposed method in this study utilizes ROP adjustment for SVO. Instead of conventional bundle adjustment, this method adopts all sets of ROPs as measurements in the designed network adjustment model. Specifically, there are six sets of ROPs among time-adjacent stereo image pairs.A SVO system was designed to implement the proposed SVO method. Two experiments were conducted in outdoor and indoor test fields to evaluate the performance. Several ground check points were set up for distance and position verification. The drift ratio was also evaluated. The results demonstrate that the designed SVO system has great feasibility and accuracy for navigation applications.

Miniaturized lumped‐element low‐temperature co‐fired ceramic balun using capacitance‐enhanced capacitors

SummaryA capacitance‐enhanced capacitor (CEC) is proposed and applied to build a low‐temperature co‐fired ceramic (LTCC) balun for miniaturization. The capacitance enhancement is by adding horizontal fingers to traditional vertically interdigital capacitor (VIC) for capacitance increasing without size enlargement. The proposed CEC increased 7.2% capacitance comparing with that of traditional VIC. The balun using the proposed CEC is only 9.5 × 8.4 × 1 mm3, which is equivalent to 0.0034 × 0.0031 × 0.0004 λg3. The measured inband S11, S21, and S31 are better than −15, −3.6, and 3.5 dB from 46 to 58 MHz, respectively. A wide fractional bandwidth (FBW) of 24% based on 15 dB return loss is also reached. The proposed balun has widely applications in maritime navigation, aircraft navigation, and 1–1.5 Tesla (T) magnetic resonance imaging (MRI) applications.

Autonomous Instrumental Error Correction for a Strapdown Inertial Navigation System

Microelectromechanical systems (MEMS)-based strapdown navigation systems offer advantages such as small size, low cost, and minimal power consumption. However, MEMS sensors are prone to significant low-frequency noise and poor bias repeatability, which can lead to navigational errors over time. These errors make them unsuitable for autonomous navigation applications, even with frequent recalibration. One way to solve this problem is the rotation modulation method. This approach is widely recognized but has only been successful with precise laser and fiber optic gyroscopes equipped with precise rotating platforms. This article focuses on the potential of adapting the rotation modulation method for the case of inexpensive MEMS sensors that can significantly improve navigation performance while maintaining the benefits of microelectromechanical technologies. Potential issues of implementation were discussed, and corresponding requirements were formulated. The proposed optimal computation scheme was verified during static tests of the developed inertial measurement unit (IMU). The rotation of the IMU is capable of harmonically modulating the quasi-static errors of inertial sensors, which are practically eliminated during the navigational algorithm processing estimation, which paves the way for a significant reduction of its navigation errors and increases its autonomous operation time. The static tests conducted in laboratory conditions confirmed the research method's high technical potential. It has been shown that the proposed method is effective for various spatial orientations of the rotational modulation axis. Installing an IMU on a non-steering car wheel allows for abandoning an artificial rotational motion source while preserving the proposed method's advantages. The proposed implementation's main limitation is that the IMU's output data update frequency becomes a multiple of the frequency of the modulating rotation. Accordingly, this frequency should be at least twice the maximum frequency of body manoeuvres to ensure their observability. Additionally, a critical issue is the compensation of the angular velocity of modulation from gyroscope readings. However, despite the mentioned considerations, such an implementation of IMU modulation by rotation still has enormous potential for application in terrestrial navigation.