Car Navigation Research Articles

EHyPRETo: Enhanced Hybrid Pre-Trained and Transfer Learning-based Contextual Relation Classification Model

Introduction: relation classification (RC) plays a crucial role in enhancing the understanding of intricate relationships, as it helps with many NLP (Natural Language Processing) applications. To identify contextual subtleties in different domains, one might make use of pre-trained models. Methods: to achieve successful relation classification, a recommended model called eHyPRETo, which is a hybrid pre-trained model, has to be used. The system comprises several components, including ELECTRA, RoBERTa, and Bi-LSTM. The integration of pre-trained models enabled the utilisation of Transfer Learning (TL) to acquire contextual information and complex patterns. Therefore, the amalgamation of pre-trained models has great importance. The major purpose of this related classification is to effectively handle irregular input and improve the overall efficiency of pre-trained models. The analysis of eHyPRETo involves the use of a carefully annotated biological dataset focused on Indian Mosquito Vector Biocontrol Agents. Results: the eHyPRETo model has remarkable stability and effectiveness in categorising, as evidenced by its continuously high accuracy of 98,73 % achieved during training and evaluation throughout several epochs. The eHyPRETo model's domain applicability was assessed. The obtained p-value of 0,06 indicates that the model is successful and adaptable across many domains. Conclusion: the suggested hybrid technique has great promise for practical applications such as medical diagnosis, financial fraud detection, climate change analysis, targeted marketing campaigns, and self-driving automobile navigation, among others. The eHyPRETo model has been developed in response to the challenges in RC, representing a significant advancement in the fields of linguistics and artificial intelligence

A Multi-Party Conversation-Based Effective Robotic Navigation System for Futuristic Vehicle

In response to the growing need for advanced in-car navigation systems that prioritize user experience and aim to reduce driver cognitive workload, this study addresses the research question of how to enhance the interaction between drivers and navigation systems. The focus is on minimizing distraction while providing personalized and geographically relevant information. The research introduces an innovative in-car robotic navigation system comprising three subsystem models: geofencing,personalization, and conversation. The dynamic geofencing model acquires geographic details related to the user's current location and provides information about required destinations. The personalization model tailors suggestions based on user preferences, while the conversation model, employing two virtual robots, fosters interactive multiparty conversations aligned with the driver's interests. The study's scope is specifically confined to interactive conversations centered on nearby restaurants and the driver's dietary preferences. Evaluation of the system indicates a notable prevalence of neutral expressions amongparticipants during interaction, suggesting that the implemented system successfully mitigates cognitive workload. Participants in the experiments express higher usability and interactivity levels, as evidenced by feedback collected at the study's conclusion, affirming the system's effectiveness in enhancing the user experience while maintaining a driver-friendly environment.
 Keywords: Human-Robot Interaction, Multiparty Conversation, In-Car Navigation

Navigation System for Personal Mobility Vehicles Following a Cluster of Pedestrians in a Corridor Using Median of Candidate Vectors Observer

In recent years, personal mobility vehicles have been required to operate autonomously in places with numerous pedestrians. A navigation method using a single human-following scheme is used to avoid collision with pedestrians. However, in many cases, a single human-following method cannot be successfully used for guidance. In crowded places, pedestrians do not always keep walking in the desired direction a user wants to go, and the vehicle must change the target pedestrian frequently. Instead of following a single pedestrian, we propose a method for the vehicle to follow a cluster of pedestrians for stable and robust following. First, the pedestrians around the vehicle are detected by multiple RGB-D cameras, and the pedestrians are tracked using YOLO and Deep Sort. Pedestrians are classified according to their walking direction, and the cluster of pedestrians walking toward the goal is selected and followed. However, the position of pedestrian is sometimes lost in occlusions and the accuracy of the walking direction depends on the distance and pose detected by the sensors. A notable problem is that the cluster of pedestrians is unstable in the cluster following; therefore, a median of candidate vectors (MCV) observer is used to remove outliers caused by observation errors. The proposed method is applied to a scenario involving pedestrians walking toward an elevator hall in a building, and its effectiveness is verified through experiments.

EVALUATING NAVIGATION PERFORMANCE OF ELASTICALLY CONSTRUCTED HD MAP WITH MULTI-SENSOR FUSION ENGINE SYSTEM

Abstract. In response to the rapid development of autonomous vehicles and the increasing demand for HD maps, the conventional mapping processes following HD maps guidelines require significant manpower and time resources. Therefore, we propose flexible procedures and methods for HD maps creation, aiming to reduce cost expenditure by employing diverse source of ground control point, sensor data collection, and mapping algorithm. This approach accelerates the production speed and capability of HD maps. In this study, we select Taiwan's National Highway No. 8 as the trial field for the elastic HD map construction method, and equipped with autonomous vehicle-grade GNSS, IMU, and LiDAR systems.We align the constructed-map data to the global coordinate system, in order to realize the concept of control point cloud map. To assess the assistance and correction capabilities of HD maps in autonomous vehicle navigation systems, we conduct accuracy evaluation through both direct and indirect methods, and analyse the strengths and weaknesses of each approach. The analysis result demonstrates that the elastic method-built HD maps not only meet the mapping accuracy requirements specified in the HD maps verification and validation guidelines, but also assist autonomous vehicles in realizing positioning, navigation, and timing with “where in lane” level (0.5 meter) accuracy.

Local multiset dimension of corona product on tree graphs

One of the topics of distance in graphs is resolving set problem. This topic has many applications in science and technology namely navigation robots, chemistry structure, and computer sciences. Suppose the set [Formula: see text], the vertex representations of [Formula: see text] is [Formula: see text], where [Formula: see text] is the length of the shortest path of the vertex [Formula: see text] and the vertex in [Formula: see text] together with their multiplicity. The set W is called a local [Formula: see text]-resolving set of graphs G if [Formula: see text] for [Formula: see text]. The local [Formula: see text]-resolving set having minimum cardinality is called the local multiset basis and its cardinality is called the local multiset dimension of [Formula: see text], denoted by [Formula: see text]. In our paper, we determine the establish bounds of local multiset dimension of graph resulting corona product of tree graphs.

Understanding Map Misinterpretation: Factors Influencing Correct Map Reading and Common Errors

Misinterpreting maps can have serious consequences, especially in situations requiring quick decisions like using car navigation systems. Studies indicate that a map reader’s experience is crucial for understanding maps, but factors such as age, education, and gender can also influence interpretation. However, understanding only the proportion of correctly interpreted information is not enough. It is essential to investigate the types of mistakes made and their causes. To address this, we conducted a study available in six languages with 511 participants who completed an online questionnaire testing their map reading skills. The questions focused on scale usage, mental rotation, and recognizing map categories (relief, line and point symbols, and geographic names). Gender had significant relation with one skill, qualification with two and age with three. Experience was associated to the highest number of skills, a total of four, confirming previous findings. When making mistakes, participants tended to overestimate distances and struggled with conceptual similarities in symbol recognition. Experienced readers often misplaced reference locations of geographic names. The results of the research could be used in the design of large-scale maps (e.g., car navigation), as they allow to reduce typical map reading errors by careful selection of symbol types and placements.

The method of inertial-visual orientation of the aircraft

The article proposes a method for determining the orientation angles of an aerial vehicle based on information from an optical camera and a triad of angular velocity sensors. The basic formulae necessary for determining the orientation angles and calculating the size of the image area for processing are given. Simulation of the operation of the algorithm for determining the pitch angle of the airial vehicle on real images was carried out. At the same time, the errors of sensors were taken into account. The use of angular velocity sensors of low and medium accuracy class was considered. The advantages of the method are the absence of the need to detect the horizon line and low requirements for the navigation computer.

The Implementation of Precise Point Positioning (PPP): A Comprehensive Review.

High-precision positioning from Global Navigation Satellite Systems (GNSS) has garnered increased interest due to growing demand in various applications, like autonomous car navigation and precision agriculture. Precise Point Positioning (PPP) offers a distinct advantage over differential techniques by enabling precise position determination of a GNSS rover receiver through the use of external corrections sourced from either the Internet or dedicated correction satellites. However, PPP's implementation has been challenging due to the need to mitigate numerous GNSS error sources, many of which are eliminated in differential techniques such as Real-Time Kinematics (RTK) or overlooked in Standard Point Positioning (SPP). This paper extensively reviews PPP's error sources, such as ionospheric delays, tropospheric delays, satellite orbit and clock errors, phase and code biases, and site displacement effects. Additionally, this article examines various PPP models and correction sources that can be employed to address these errors. A detailed discussion is provided on implementing the standard dual-frequency (DF)-PPP to achieve centimeter- or millimeter-level positioning accuracy. This paper includes experimental examples of PPP implementation results using static data from the International GNSS Service (IGS) station network and a kinematic road test based on the actual trajectory to showcase DF-PPP development for practical applications. By providing a fusion of theoretical insights with practical demonstrations, this comprehensive review offers readers a pragmatic perspective on the evolving field of Precise Point Positioning.

Dual-antenna GNSS-aided robust MEMS IMU initial alignment under the earth-centered-earth-fixed frame

The micro-electro-mechanical system (MEMS), with its small size, low cost and low power, has attracted widespread attentions in recent year, and the initial alignment of the inertial measurement unit (IMU) is an indispensable step before the navigation computation. In the traditional alignment methods with only the velocity measurements, the yaw angle is subject to divergence under low dynamic scenarios, and the measurements are also susceptible to the external environments. Furthermore, the navigation frame (n frame) is usually chosen as the reference frame. Although intuitive, it is more computationally complex compared with the earth-centered-earth-fixed frame (e frame). Therefore, to improve the alignment performance and computational efficiency, a robust MEMS IMU initial alignment method with yaw assistance under the e frame is proposed. In this method, to address the issues of the yaw divergency and computational inefficiency in the traditional methods, the relationship between the yaw angle, derived from dual-antenna global navigation satellite system baseline solution, and IMU error is derived and incorporated into the state space model with e frame as the reference frame. Besides, a robust extended Kalman filtering is adopted to further improve the system’s performance of outlier resistance, whose robust factors are determined by the Institute of Geodesy and Geophysics Ⅲ model. To validate the performance of the proposed method, two field experiments with the velocity of about 0.7 m s−1 and 1.5 m s−1 was conducted, and the collected data were processed for contrastive analysis. The results show that the effectiveness of the proposed method at suppressing yaw angle divergence and improving the alignment accuracy by resisting outliers under low dynamic scenarios compared with the traditional method. Moreover, the elapsed time is reduced by about 11% when the reference frame in data processing is switched from the n frame to the e frame.

An in-motion alignment method of AUV SINS/DVL navigation system based on FGO

The navigation system of automatic underwater vehicle (AUV) usually needs to wait for about 20 min for initial alignment before running. However, most of in-motion alignment methods have no re-integration process, so the positioning error caused by early attitude error will remain in the positioning result.Besides, current in-motion alignment methods are usually based on recursive Extended Kalman Filter (EKF). In nonlinear systems, the accuracy of recursive optimization will be reduced.In this study, a method of in-motion alignment using factor graph is proposed. This method optimizes the states of the whole trajectory in batch. Experiments show that the position accuracy of the method proposed is much better than the existing method.

Res-SwinTransformer with Local Contrast Attention for Infrared Small Target Detection

Infrared small target detection for aerial remote sensing is crucial in both civil and military fields. For infrared targets with small sizes, low signal-to-noise ratio, and little detailed texture information, we propose a Res-SwinTransformer with a Local Contrast Attention Network (RSLCANet). Specifically, we first design a SwinTransformer-based backbone to improve the interaction capability of global information. On this basis, we introduce a residual structure to fully retain the shallow detail information of small infrared targets. Furthermore, we design a plug-and-play attention module named LCA Block (local contrast attention block) to enhance the target and suppress the background, which is based on local contrast calculation. In addition, we develop an air-to-ground multi-scene infrared vehicle dataset based on an unmanned aerial vehicle (UAV) platform, which can provide a database for infrared vehicle target detection algorithm testing and infrared target characterization studies. Experiments demonstrate that our method can achieve a low-miss detection rate, high detection accuracy, and high detection speed. In particular, on the DroneVehicle dataset, our designed RSLCANet increases by 4.3% in terms of mAP@0.5 compared to the base network You Only Look Once (YOLOX). In addition, our network has fewer parameters than the two-stage network and the Transformer-based network model, which helps the practical deployment and can be applied in fields such as car navigation, crop monitoring, and infrared warning.

Smartphone MEMS Accelerometer and Gyroscope Measurement Errors: Laboratory Testing and Analysis of the Effects on Positioning Performance.

Embedding various sensors with powerful computing and storage capabilities in a small communication device, smartphones have become a prominent platform for navigation. With the increasing popularity of Apple CarPlay and Android Auto, smartphones are quickly replacing built-in automotive navigation solutions. On the other hand, smartphones are equipped with low-performance Micro Electro Mechanical Systems (MEMS) sensors to enhance their navigation performance in Global Navigation Satellite System (GNSS)-degraded or -denied environments. Compared with higher-grade inertial navigation systems (INS), MEMS-based INSs have a poor navigation performance due to large measurement errors. In this paper, we present laboratory test results on the stochastic and deterministic errors observed in MEMS inertial sensor measurements of five different smartphones from different manufacturers. Then, we describe and discuss the short-term effects of these errors on the pure inertial navigation performance and also on the navigation performance based on the tight coupling of INS with GNSS measurements using a smartphone.

Effects of repetitive low-acceleration impacts on attitude estimation with micro-electromechanical inertial measurement units.

Inertial Measurement Units are present in several applications in aerospace, unmanned vehicle navigation, legged robots, and human motion tracking systems, due to their ability to estimate a body's acceleration, orientation and angular rate. In contrast to rovers and drones, legged locomotion involves repeated impacts between the feet and the ground, and rapid locomotion (e.g., running) involves alternating stance and flight phases, resulting in substantial oscillations in vertical acceleration. The aim of this research is to investigate the effects of periodic low-acceleration impacts (4 g, 8 g and 16 g), which imitate the vertical motion of a running robot, on the attitude estimation of multiple Micro-Electromechanical Systems IMUs. The results reveal the presence of a significant drift in the attitude estimation of the sensors, which can provide important information during the design process of a robot (sensor selection), or during the control phase (e.g., the system will know that after a series of impacts the attitude estimations will be inaccurate).

Toward a deep CNN and RS‐GOA framework for vehicle detection, traffic flow estimation, and optimal path selection from surveillance videos

SummarySeveral autonomous traffic monitoring systems have been created as a result of the growing number of vehicles in urban areas. Traffic surveillance systems that use roadside cameras, in particular, are becoming widely used for traffic management. For an efficient traffic control and vehicle navigation system, accurate traffic flow information must be obtained based on the vehicles detected in surveillance videos. However, vehicles of various scales are difficult to spot in traffic surveillance videos due to the presence of barricades, other vehicles, and the impact of poor lighting. Also, adverse weather conditions like snow, fog, and heavy rain diminish the visual quality of the surveillance footage. This paper proposes multi‐scale dense nested deep CNN (MSDN‐DCNN) and regional search grasshopper optimization algorithm (RS‐GOA) framework to accurately detect the vehicles, estimate the traffic flow, and find the optimal path with less travel time. First, the surveillance videos are pre‐processed, which includes frame conversion, redundancy removal, and image enhancement. The pre‐processed frames are given as input to the MSDN‐DCNN for multi‐scale vehicle detection. The detected results are used for vehicle counting and estimating the traffic flow. Finally, the optimal path is chosen based on the traffic flow information by using the RS‐GOA algorithm. The performance of the proposed method is compared with the existing vehicle detection and path selection techniques. The results illustrate that the proposed Deep CNN‐RS‐GOA framework has improved performance with high detection accuracy (91.03%), high speed (53.9 fps), less running time (1,000 ms), less travel time, and faster convergence.

Investigation of Accuracy of TOA and SNR of Radio Pulsar Signals for Vehicles Navigation.

It is known that X-ray and gamma-ray pulsars can only be observed by spacecraft because signals from these pulsars are impossible to be detected on the Earth's surface due to their strong absorption by the Earth's atmosphere. The article is devoted to the theoretical aspects regarding the development of an autonomous radio navigation system for transport with a small receiving antenna, using radio signals from pulsars, similar to navigation systems for space navigation. Like GNSS systems (X-ray and radio), they use signals from four suitable pulsars to position the object. These radio pulsars (out of 50) are not uniformly distributed but are grouped in certain directions (at least 6 clusters can be determined). When using small antennas (with an area of up to tens of square meters) for pulsar navigation, the energy of the pulsar signals received within a few minutes is extremely insufficient to obtain the required level of SNR at the output of the receiver to form TOA estimation, ensuring positioning accuracy up to tens of kilometers. This is one of the scientific tasks that is solved in the paper by studying the relationship between the SNR of the receiver output, which depends on the size of the antenna, the type of signal processing, and the magnitude of the TOA accuracy estimate. The second scientific task that is solved in the paper is the adaptation of all the possible approaches and algorithms suggested in the statistical theory of radars in the suggested signal algorithm for antenna processing and to evaluate the parameters of the TOA and DS pulsar signals, in order to increase the SNR ratio at the receiver output, while preserving the dimensions of the antenna. In this paper, the functional structure of signal processing in a pulsar transport navigation system is proposed, and the choice of the observed second and millisecond pulsars for obtaining a more accurate TOA estimate is discussed. The proposed estimates of positioning accuracy (TOA only, no phase) in an autonomous pulsar vehicle navigation system would only be suitable for the navigation of large vehicles (sea, air, or land) that do not require accurate navigation at sea, air, or desert. Large-sized antennas with an area of tens of square meters to hundreds of square meters can be installed in such vehicles.

Using Congestion to Improve Short-Term Velocity Forecasting with Machine Learning Models

The ability to estimate future velocity on a road network is relevant for applications such as vehicle navigation systems and emergency vehicle dispatching. The existence of traffic congestion severely impacts travellers’ travel time. In this paper, we investigate the use of congestion prediction in velocity forecasting models. Using a data-driven approach, we classify traffic observations into classes with and without congestion. We find that this classification improves velocity forecasting, showing that using congestion as an attribute reduces the MAE by at least 6.15% for different machine and deep learning models including random forests, multi-layer perceptrons and recurrent neural networks. We propose a random forest model that identifies the future congestion state from past traffic velocity and volume data and then use it to build new short-term velocity forecasting models. These models reduce the MAE prediction error up to 3.37% over the best models that do not consider congestion. This improvement represents overcoming a 53.75% of the error due to not precisely knowing the future congestion state.

A Novel Method for Extracting and Analyzing the Geometry Properties of the Shortest Pedestrian Paths Focusing on Open Geospatial Data

Unlike car navigation, where almost all vehicles can traverse every route, one route might not be optimal or even suitable for all pedestrians. Route geometry information, including tortuosity, twists and turns along roads, junctions, and road slopes, among others, matters a great deal for specific types of pedestrians, particularly those with limited mobility, such as wheelchair users and older adults. Offering practical routing services to these users requires that pedestrian navigation systems provide further information on route geometry. Therefore, this article proposes a novel method for extracting and analyzing the geometry properties of the shortest pedestrian paths, with a focus on open geospatial data across four aspects: (a) similarity, (b) route curviness, (c) road turns and intersections, and (d) road gradients. Deriving from the Hausdorff distance, a metric called the “dissimilarity ratio” was developed, allowing us to determine whether pairs of routes show any tendencies to be similar to each other. Using the “sinuosity index”, a segment-based technique quantified the route curviness based on the number and degree of the road turns along the route. Moreover, relying upon open elevation data, the road gradients were extracted to identify routes offering smoother motion and better accessibility. Lastly, the road turns and intersections were investigated as pedestrian convenience and safety indicators. A local government area of Greater Sydney in Australia was chosen as the case study. The analysis was implemented on OpenStreetMap (OSM) shortest pedestrian paths against Google Maps as a benchmark for real-world commercial applications. The similarity analysis indicated that over 90% of OSM routes were identical or roughly similar to Google Maps. In addition, while Spearman’s rank correlation showed a direct relationship between route curviness and route length, rS(758) = 0.92, p < 0.001, OSM, on average, witnessed more tortuous routes and, consequently, shorter straight roads between turns. However, OSM routes could be more suitable for pedestrians when the frequency of intersections and road slopes are at the center of attention. Finally, the devised metrics in this study, including the dissimilarity ratio and sinuosity index, showed their practicability in translating raw values into meaningful indicators.

A flexible and scalable single-level framework for OD matrix inference using IoT data

This study proposes a flexible and scalable single-level framework for origin–destination matrix (ODM) inference using data from IoT (Internet of Things) and other sources. The framework allows the analyst to integrate information from multiple data sources, while controlling for differences in data quality across sources. We assess the effectiveness of the framework through a real-world experiment in Greater Adelaide (GA), Australia. We infer car OD flows within the region using four separate data sources: site-level traffic counts from loop detectors, vehicle trajectories recorded by roadside Bluetooth sensors, partial OD flows based on data from in to vehicle navigation systems, and journey-to-work OD data collected by the Australian Census. We compare our OD inferences with those from the current version of the Strategic Adelaide Model (SAM), calibrated using data from traditional household travel surveys. We find remarkable consistency between our inferences and those from SAM, despite differences in input data and methodologies. For example, for the morning peak period, we predict the total number of trips made within GA to be equal to 556,000, while the corresponding prediction from SAM is equal to 484,000. The two predictions are within 15 per cent of each other. When we compare the spatial distribution of trips, in terms of origins and destinations, we find that our inferred OD matrix has an 86 per cent cosine similarity to the corresponding SAM matrix. Travel time predictions based on our OD inferences correspond closely to those predicted by the Google Directions API, lending further confidence in our inferences. In summary, our results show that the proposed framework can produce highly comparable ODM, trip production and trip attraction patterns to those inferred from traditional household travel survey-based transportation demand modelling methods.

Retraction: Al Said, N, Gorbachev, Y, Avdeenko, A. An unmanned aerial vehicles navigation system on the basis of pattern recognition applications—Review of implementation options and prospects for development. Software: Practice and Experience 2021; 51: 1509–1517. https://doi.org/10.1002/spe.2964

The above article, published online on July 2021 in Wiley Online Library https://onlinelibrary.wiley.com/doi/10.1002/spe.2964 has been retracted by agreement between the journal's Editor‐in‐Chief and John Wiley & Sons Ltd.The retraction has been agreed following concerns regarding manipulation of the peer review and publishing process. Concerns were originally raised by a third party (1). Further investigation by the publisher has found manipulation of the peer review process. The retraction has been agreed because the peer review of the article was compromised. The authors disagree with this retraction. Abalkina, A. (2022). Publication and collaboration anomalies in academic papers originating from a paper mill: evidence from a Russia‐based paper mill https://arxiv.org/abs/2112.13322

A Curbside Parking Planning and Searching Navigation System for Autonomous Vehicles

A Curbside Parking Planning and Searching Navigation System for Autonomous Vehicles