Navigation Assistance Research Articles

Contextual Route Recommendation System in Heterogeneous Traffic Flow

The traffic composition in developing countries comprises of variety of vehicles which include cars, buses, trucks, and motorcycles. Motorcycles dominate the road with 77.5% compared to other types. Meanwhile, route recommendation such as navigation and Advanced Driver Assistance Systems (ADAS) is limited to particular vehicles only. In this research, we propose a framework for a contextual route recommendation system that is compatible with traffic conditions and vehicle type, along with other relevant attributes (traffic prediction, weather, temperature, humidity, heterogeneity, current speed, and road length). The framework consists of two phases. First, it predicts the traffic conditions by using Knowledge-Growing Bayes Classifier on which the dataset is obtained from crawling the public CCTV feeds and TomTom digital map application for each observed road. The performances of the traffic prediction are around 60.78–73.69%, 63.64–77.39%, and 60.78–73.69%, for accuracy, precision, and recall respectively. Second, to accommodate the route recommendation, we simulate and utilize a new measure, called road capacity value, along with the Dijkstra algorithm. By adopting the compatibility, the simulation results could show alternative paths with the lowest RCV (road capacity value).

Abstracting routes to their route-defining locations

Today's navigation assistance systems provide turn-by-turn instructions, which only focus on the next decision to take without offering any larger context. Information presentation is uniform and disconnected, i.e., any local instruction is equally important and not linked to any other decisions or the overall route context. This differs from how people usually give instructions and hinders spatial learning. In order to (re-)establish this larger context, we present an approach to identifying those locations along a route that define its characteristics, termed route-defining locations. These are prominent, easily recognized locations, which help relating the route to an environment's overall structure and a navigator's existing knowledge about the environment. The approach allows for determining route-defining locations on different levels of detail. Thus, at the same time it offers a mechanism for simplifying (or abstracting) a route. In this paper, we particularly focus on the latter aspect, presenting in detail the approach for identifying route-defining locations. For a given route, we, first, simplify its shape to extract those turns along the route that characterize its overall shape. Then, we rank landmarks and streets along the route based on their prominence. Finally, we include in the simplified route those turns that correspond to the locations of the most prominent landmarks and streets. The result is a set of route-defining locations extracted from the route shape, and prominent landmarks and streets along the route. In an agent-based simulation, we then evaluate the approach's ability to abstract a route to its characteristics, i.e., the defining locations. Results show that, indeed, our approach is effective in that respect, but success depends on ‘matching’ abstraction to an agent's knowledge about the environment.

An Overview on Automated Vehicles

A self-driving car is a machine that can really hear and function without the need for human intervention. A human rider is never needed to maintain control of the vehicle or to be in the vehicle at the same time. A self-employed vehicle should be able to do all of the duties that a competent human driver can. A standard vehicle should be capable of traveling to any place. Thousands of hours have been recorded on US highways by self-driving vehicles, such as Google's robotic vehicle prototype, but they are not yet commercially accessible. Self-sufficient vehicles use a range of technologies. It may be developed to assist navigation using GPS sensor data. Sensors and other equipment should be used to avoid accidents. And also use a variety of technologies, such as extended truth, in which a vehicle delivers data to drivers in novel and innovative ways. Many people think that substantial autonomous vehicle development will aggravate current man-made car insurance and traffic regulations. Extensive research on autonomous cars is taking place not just in the United States, but also in Europe and other areas of the world. According to industry analysts, it will only be a matter of time until individuals are able to use a device on a regular basis as a result of such advances. The construction and device design of an autonomous vehicle, as well as the control system module that enables the vehicle to travel independently, are covered in this article. The CaRINA I model was used to test and validate the team's autonomous navigation and driver assistance technologies. Our technology covers modifications to mechanical vehicles as well as the creation and implementation of an integrated computer architecture.

Maritime navigational assistance by visual augmentation

AbstractSeveral types of equipment have been developed over the years to assist ship operators with their tasks. Nowadays, navigational equipment typically provides an enormous volume of information. Thus, there is a corresponding need for efficiency in how such information is presented to ship operators. Augmented reality (AR) systems are being investigated for such efficient presentation of typical navigational information. The present work is particularly interested in an AR architecture commonly referred as monitor augmented reality (MAR).In this context, the development of MAR systems is briefly summarised. The projection of three-dimensional elements into a camera scene is presented. Potential visual assets are proposed and exemplified with videos from a ship manoeuvring simulator and a real experiment. Enhanced scenes combining pertinent virtual elements are shown exemplifying potential assistance applications. The authors mean to contribute to the popularisation of MAR systems in maritime environments. Further research is suggested to define optimal combinations of visual elements for alternative maritime navigation scenarios. Note that there are still many challenges for the deployment of MAR tools in typical maritime operations.

Adaptive Fuzzy Game-Based Energy-Efficient Localization in 3D Underwater Sensor Networks

Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neuro-fuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg–Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay.

Road Curb Detection: A Historical Survey.

Curbs are used as physical markers to delimit roads and to redirect traffic into multiple directions (e.g., islands and roundabouts). Detection of road curbs is a fundamental task for autonomous vehicle navigation in urban environments. Since almost two decades, solutions that use various types of sensors, including vision, Light Detection and Ranging (LiDAR) sensors, among others, have emerged to address the curb detection problem. This survey elaborates on the advances of road curb detection problems, a research field that has grown over the last two decades and continues to be the ground for new theoretical and applied developments. We identify the tasks involved in the road curb detection methods and their applications on autonomous vehicle navigation and advanced driver assistance system (ADAS). Finally, we present an analysis on the similarities and differences of the wide variety of contributions.

A novel index for real-time ship collision risk assessment based on velocity obstacle considering dimension data from AIS

An index for real-time collision risk assessment is the basis for the safe navigation of ships. To consider dimension related factors more accurately and objectively in the collision risk index, and to enhance the readability of the index in collision avoidance decision-making, we propose a novel index by combining the dimension data from automatic identification system (AIS) and velocity obstacle approach, namely the ‘margin of projected collision (MPC)’ index. Compared with traditional existing indicators, our proposed index can more accurately and objectively judge whether the two ships collide if the current state of motion is maintained. Also, the numerical meaning of quantified risk is innovatively directional in the state of motion (i.e., course and speed), so the proposed index may additionally provide navigation assistance information including proactive warnings for dangerous behaviour and behaviour references for collision avoidance. In addition, through the real encounter cases with high collision risk, we demonstrate that our proposed index has high practical value and can be used as a good supplement to the traditional collision risk index, especially in high-precision applications such as waters with heavy maritime traffic and maritime autonomous surface ships (MASSs).

An Intelligent System for the Enhancement of Visually Impaired Navigation and Disaster Assistance using Geo-Based Positioning and Machine Learning

Technological advancement has brought the masses unprecedented convenience, but unnoticed by many, a population neglected through the age of technology has been the visually impaired population. The visually impaired population has grown through ages with as much desire as everyone else to adventure but lack the confidence and support to do so. Time has transported society to a new phase condensed in big data, but to the visually impaired population, this quick-pace living lifestyle, along with the unpredictable nature of natural disaster and COVID-19 pandemic, has dropped them deeper into a feeling of disconnection from the society. Our application uses the global positioning system to support the visually impaired in independent navigation, alerts them in face of natural disasters, and reminds them to sanitize their devices during the COVID-19 pandemic.

NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences.

Visual place recognition (VPR) is critical in not only localization and mapping for autonomous driving vehicles, but also assistive navigation for the visually impaired population. To enable a long-term VPR system on a large scale, several challenges need to be addressed. First, different applications could require different image view directions, such as front views for self-driving cars while side views for the low vision people. Second, VPR in metropolitan scenes can often cause privacy concerns due to the imaging of pedestrian and vehicle identity information, calling for the need for data anonymization before VPR queries and database construction. Both factors could lead to VPR performance variations that are not well understood yet. To study their influences, we present the NYU-VPR dataset that contains more than 200,000 images over a 2km×2km area near the New York University campus, taken within the whole year of 2016. We present benchmark results on several popular VPR algorithms showing that side views are significantly more challenging for current VPR methods while the influence of data anonymization is almost negligible, together with our hypothetical explanations and in-depth analysis.

Trends of Utilization and 90-Day Complication Rates for Computer-Assisted Navigation and Robotic Assistance for Total Knee Arthroplasty in the United States From 2010 to 2018.

BackgroundComputer-assisted navigation (CAN) and robotic assistance (RA) for total knee arthroplasty (TKA) are gaining in popularity. The purpose of this study is to update the literature on United States technology-assisted TKA trends of national utilization, regional utilization, and 90-day complication rates requiring readmission.MethodsPatients who underwent primary, elective TKA between 2010 and 2018 were retrospectively identified in the PearlDiver All Payer Claims Database (PearlDiver Technologies Inc.). TKAs were classified as conventional, CAN, or RA based on International Classification of Diseases nineth or tenth revision and Current Procedural Technology codes. Annual rates and regional trends of each type of TKA were reported. Ninety-day complications requiring readmission for each group were captured. Multivariable logistic regression was used to identify odds ratios (OR) for all-cause readmission based on TKA modality.ResultsOf the 1,307,411 elective TKAs performed from 2010 to 2018, 92.8% were conventional, and 7.7% were technology-assisted (95.2% CAN and 4.9% RA). RA-TKA had the greatest increase in utilization (+2204%). The Western region had the highest utilization of technologies for TKA, while the Midwestern region had the lowest. Ninety-day postoperative complications requiring readmission were highest for conventional TKA and lowest for RA-TKA. RA-TKA (OR 0.68; 97.5% confidence interval 0.56-0.83, P < .001) and CAN-TKA (OR 0.93; 97.5% confidence interval 0.88-0.97, P < .05) had significantly lower odds of all-cause 90-day complications requiring readmission than conventional TKA.ConclusionUtilization of RA-TKA and CAN-TKA continues to rise across the United States. The use of these technologies is associated with a lower OR of readmission within 90 days postoperatively.

Landmark-based navigation instructions improve incidental spatial knowledge acquisition in real-world environments

The repeated use of navigation assistance systems leads to decreased processing of the environment. Previous studies demonstrated that auditory references to landmarks in navigation instructions can improve incidental spatial knowledge acquisition when driving a single route through an unfamiliar virtual environment. Based on these results, three experiments were conducted to investigate the generalizability and ecological validity of incidental landmark and route knowledge acquisition induced by landmark-based navigation instructions.In the first experiment, spatial knowledge acquisition was tested after watching an interactive video showing the navigation of a real-world urban route. A second experiment investigated incidental spatial knowledge acquisition during assisted navigation when participants walked through the same real-world, urban environment. The third experiment tested the acquired spatial knowledge two weeks after participants had walked through the real-world environment.All experiments demonstrated better performance in a cued-recall task for participants navigating with landmark-based navigation instructions as compared to standard instructions. Different levels of information provided with landmark-based instructions impacted landmark recognition dependent on the delay between navigation and test. The results replicated an improved landmark and route knowledge when using landmark-based navigation instructions emphasizing that auditory landmark augmentation enhances incidental spatial knowledge acquisition, and that this enhancement can be generalized to real-life settings.This research is paving the way for navigation assistants that, instead of impairing spatial knowledge acquisition, incidentally foster the acquisition of landmark and route knowledge during every-day navigation.

Sialendoscopy and CT navigation assistance in the surgery of sialolithiasis.

BackgroundA sialendoscopy-assisted combined approach is well established in the surgery of sialolithiasis. In cases of proximal salivary stones, transcutaneous sialendoscopy-assisted extractions with parotid and submandibular gland preservation is the primary intention of treatment. We recently added computer tomography (CT) navigation to improve the results of this challenging surgery equally in both localizations.Patients and methodsAl l the patients who submitted to sialendoscopy and sialendoscopy-assisted procedures at the tertiary institution between January 2012 and October 2020 were included in the present study. From November 2019, CT navigation was added in cases with sialolithiasis and a presumably poor sialendoscopic visibility. We evaluated the parameters of the disease, diagnostic procedures, sialendoscopic findings and outcomes, with or without optical surgical navigation.ResultsWe performed 178 successful salivary stone removals in 372 patients, of which 118 were combined sialendos-copy-assisted approaches, including 16 transcutaneous proximal, 10 submandibular and 6 parotid stone operations. Surgical navigation was used in six patients, four times for submandibular and twice for parotid sialolithiasis. These were all non-palpable, sialendoscopically invisible or partially visible stones, and we managed to preserve five of the six salivary glands.ConclusionsThe addition of CT navigation to sialendoscopy-assisted procedures for non-palpable, sialendoscopically invisible and fixed stones is a significant advantage in managing sialolithiasis. By consistently performing sialendoscopy and related preservation procedures, we significantly reduced the need for sialoadenectomies in patients with obstructive salivary gland disease.

An RGB-D Camera Based Visual Positioning System for Assistive Navigation by a Robotic Navigation Aid

There are about 253 million people with visual impairment worldwide. Many of them use a white cane and/or a guide dog as the mobility tool for daily travel. Despite decades of efforts, electronic navigation aid that can replace white cane is still research in progress. In this paper, we propose an RGB-D camera based visual positioning system (VPS) for real-time localization of a robotic navigation aid (RNA) in an architectural floor plan for assistive navigation. The core of the system is the combination of a new 6-DOF depth-enhanced visual-inertial odometry (DVIO) method and a particle filter localization (PFL) method. DVIO estimates RNA's pose by using the data from an RGB-D camera and an inertial measurement unit (IMU). It extracts the floor plane from the camera's depth data and tightly couples the floor plane, the visual features (with and without depth data), and the IMU's inertial data in a graph optimization framework to estimate the device's 6-DOF pose. Due to the use of the floor plane and depth data from the RGB-D camera, DVIO has a better pose estimation accuracy than the conventional VIO method. To reduce the accumulated pose error of DVIO for navigation in a large indoor space, we developed the PFL method to locate RNA in the floor plan. PFL leverages geometric information of the architectural CAD drawing of an indoor space to further reduce the error of the DVIO-estimated pose. Based on VPS, an assistive navigation system is developed for the RNA prototype to assist a visually impaired person in navigating a large indoor space. Experimental results demonstrate that: 1) DVIO method achieves better pose estimation accuracy than the state-of-the-art VIO method and performs real-time pose estimation (18 Hz pose update rate) on a UP Board computer; 2) PFL reduces the DVIO-accrued pose error by 82.5% on average and allows for accurate wayfinding (endpoint position error ≤ 45 cm) in large indoor spaces.

Mobility Assistance for People With Cognitive Disabilities: Identifying Implementation Outcomes and Strategies of Innovative Technology

Abstract Date Presented Accepted for AOTA INSPIRE 2021 but unable to be presented due to online event limitations. The Mobility Assistance for People with Cognitive Disabilities (MAPCD) program, including the WayFinder 3 assistive smartphone navigation app and training program, addresses barriers to community mobility and provides supports to both individuals with cognitive deficits and their caregivers. We describe the MAPCD program and examine strategies that facilitated the adoption of the WayFinder 3 app designed to optimize independent community mobility. Primary Author and Speaker: Sarah E. Anderson Additional Authors and Speakers: Lisa Juckett Contributing Authors: Carmen DiGiovine, Sandra A. Metzler, Ashley Stojkov, Bethany Frick, Kaetlyn E. Culter, and Elizabeth Mance

Assessing the Effect of a Visual Navigational System on Route-Learning From an Ecological Perspective.

Route-learning, considered from an ecological approach to perception, is posited to involve the detection of information over time that specifies a path from one location to another. The study examines whether the use of a visual navigational system (e.g., GPS) may impede route-learning by drawing attention away from transitions along a path that serve as information for way-finding. Virtual reality (VR) technology used in conjunction with an extensive, detailed environmental simulation was employed to explore this possibility. One group of participants drove a simulated car in VR along a designated path while relying on visual GPS guidance. It was expected that use of the GPS display would draw attention away from temporally continuous path information. A second group initially drove the same route without GPS guidance. Both groups drove the path a second time without navigational assistance. Overall, the percentage of correct actions taken at intersections (transitions) during the second trial were significantly lower for the first group who initially drove the route with visual GPS guidance as compared to those who initially traveled the route without it. The results are consistent with the kind of trade-off that is commonplace when tools are used to mediate and assist skilled action.

Spatial thinking, cognitive mapping, and spatial awareness.

This article looks at wayfinding and spatial orientation as important everyday spatial thinking skills and discusses why some people have difficultywith the skills and how one can assist people with difficulty in navigation. It first clarifies the characteristics of human spatial cognition and behavior and the tendency of spatial knowledge to be distorted and fragmented in the environment. In particular, it emphasizes the existence of large individual differences in the skill of cognitive mapping, namely the accuracy of metric and configurational understanding of the environment. The article then looks at difficulties associated with the use of maps and description of spatial relations. Given these difficulties, the article discusses the possibilities of assisting people with mobile navigation tools and improving the skill of cognitive mapping by training in spatial orientation. Implications for the development of user-adapted and context-aware navigation assistance and the significance of research from an individual differences perspective are finally discussed.

Restricted Kinematic Alignment, the Fundamentals, and Clinical Applications.

Introduction: After a better understanding of normal knee anatomy and physiology, the Kinematic Alignment (KA) technique was introduced to improve clinical outcomes of total knee arthroplasty (TKA). The goal of the KA technique is to restore the pre-arthritic constitutional lower limb alignment of the patient. There is, however, a large range of normal knee anatomy. Unusual anatomies may be biomechanically inferior and affect TKA biomechanics and wear patterns. In 2011, the leading author proposed the restricted kinematic alignment (rKA) protocol, setting boundaries to KA for patients with an outlier or atypical knee anatomy.Material and Equipment: rKA aims to reproduce the constitutional knee anatomy of the patient within a safe range. Its fundamentals are based on sound comprehension of lower limb anatomy variation. There are five principles describing rKA: (1) Combined lower limb coronal orientation should be ± 3° of neutral; (2) Joint line orientation coronal alignment should be within ± 5° of neutral; (3) Natural knee's soft tissues tension/ laxities should be preserved/restored; (4) Femoral anatomy preservation is prioritized; (5) The unloaded/most intact knee compartment should be resurfaced and used as the pivot point when anatomical adjustment is required. An algorithm was developed to facilitate the decision-making.Methods: Since ~50% of patients will require anatomic modification to fit within rKA boundaries, rKA is ideally performed with patient-specific instrumentation (PSI), intra-operative computer navigation or robotic assistance. rKA surgical technique is presented in a stepwise manner, following the five principles in the algorithm.Results: rKA produced excellent mid-term clinical results in cemented or cementless TKA. Gait analysis showed that rKA TKA patients had gait patterns that were very close to a non-operated control group, and these kinematics differences translated into significantly better postoperative patient-reported scores than mechanical alignment (MA) TKA cases.Discussion: Aiming to improve the results of MA TKA, rKA protocol offers a satisfactory compromise that recreates patients' anatomy in most cases, omitting the need for extensive corrections and soft tissue releases that are often required with MA. Moreover, it precludes the reproduction of extreme anatomies seen with KA.

Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People.

Facilitating navigation in pedestrian environments is critical for enabling people who are blind and visually impaired (BVI) to achieve independent mobility. A deep reinforcement learning (DRL)–based assistive guiding robot with ultrawide-bandwidth (UWB) beacons that can navigate through routes with designated waypoints was designed in this study. Typically, a simultaneous localization and mapping (SLAM) framework is used to estimate the robot pose and navigational goal; however, SLAM frameworks are vulnerable in certain dynamic environments. The proposed navigation method is a learning approach based on state-of-the-art DRL and can effectively avoid obstacles. When used with UWB beacons, the proposed strategy is suitable for environments with dynamic pedestrians. We also designed a handle device with an audio interface that enables BVI users to interact with the guiding robot through intuitive feedback. The UWB beacons were installed with an audio interface to obtain environmental information. The on-handle and on-beacon verbal feedback provides points of interests and turn-by-turn information to BVI users. BVI users were recruited in this study to conduct navigation tasks in different scenarios. A route was designed in a simulated ward to represent daily activities. In real-world situations, SLAM-based state estimation might be affected by dynamic obstacles, and the visual-based trail may suffer from occlusions from pedestrians or other obstacles. The proposed system successfully navigated through environments with dynamic pedestrians, in which systems based on existing SLAM algorithms have failed.

Smartphone-Based Inertial Odometry for Blind Walkers.

Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers.

Prototype Design and Experimental Evaluation of Autonomous Collaborative Communication System for Emerging Maritime Use Cases.

Automated systems have been seamlessly integrated into several industries as part of their industrial automation processes. Employing automated systems, such as autonomous vehicles, allows industries to increase productivity, benefit from a wide range of technologies and capabilities, and improve workplace safety. So far, most of the existing systems consider utilizing one type of autonomous vehicle. In this work, we propose a collaboration of different types of unmanned vehicles in maritime offshore scenarios. Providing high capacity, extended coverage, and better quality of services, autonomous collaborative systems can enable emerging maritime use cases, such as remote monitoring and navigation assistance. Motivated by these potential benefits, we propose the deployment of an Unmanned Surface Vehicle (USV) and an Unmanned Aerial Vehicle (UAV) in an autonomous collaborative communication system. Specifically, we design high-speed, directional communication links between a terrestrial control station and the two unmanned vehicles. Using measurement and simulation results, we evaluate the performance of the designed links in different communication scenarios and we show the benefits of employing multiple autonomous vehicles in the proposed communication system.