A dynamic two-dimensional (D2D) weight-based map-matching algorithm

Existing map-Matching (MM) algorithms primarily localize positioning fixes along the centerline of a road and have largely ignored road width as an input. Consequently, vehicle lane-level localization, which is essential for stringent Intelligent Transport System (ITS) applications, seems difficult to accomplish, especially with the positioning data from low-cost GPS sensors. This paper aims to address this limitation by developing a new dynamic two-dimensional (D2D) weight-based MM algorithm incorporating dynamic weight coefficients and road width. To enable vehicle lane-level localization, a road segment is virtually expressed as a matrix of homogeneous grids with reference to a road centerline. These grids are then used to map-match positioning fixes as opposed to matching on a road centerline as carried out in traditional MM algorithms. In this developed algorithm, vehicle location identification on a road segment is based on the total weight score which is a function of four different weights: (i) proximity, (ii) kinematic, (iii) turn-intent prediction, and (iv) connectivity. Different parameters representing network complexity and positioning quality are used to assign the relative importance to different weight scores by employing an adaptive regression method. To demonstrate the transferability of the developed algorithm, it was tested by using 5,830 GPS positioning points collected in Nottingham, UK and 7,414 GPS positioning points collected in Mumbai and Pune, India. The developed algorithm, using stand-alone GPS position fixes, identifies the correct links 96.1% (for the Nottingham data) and 98.4% (for the Mumbai-Pune data) of the time. In terms of the correct lane identification, the algorithm was found to provide the accurate matching for 84% (Nottingham) and 79% (Mumbai-Pune) of the fixes obtained by stand-alone GPS. Using the same methodology adopted in this study, the accuracy of the lane identification could further be enhanced if the localization data from additional sensors (e.g. gyroscope) are utilized. ITS industry and vehicle manufacturers can implement this D2D map-matching algorithm for liability critical and in-vehicle information systems and services such as advanced driver assistant systems (ADAS)

Fusion of Digital Road Maps with Inertial Sensors and Satellite Navigation Systems Using Kalman Filter and Hidden Markov Models

Fusion of low-cost/low-power MEMS accelerometer/gyroscope sensors with Global Navigation Satellite Systems (GNSSs) is commonly used for vehicular localization, internet of things (IoT) tracking and Location-based Services (LBS). However, robust localization in dense urban areas is challenging due to GNSS service interruptions and satellite signal blockage. To overcome this problem, this paper describes a map-aided MEMS Accelerometer/Gyroscope/GNSS sensor fusion system for enhanced localization in dense urban areas under long GNSS outages. The work applies Extended Kalman Filter (EKF) to fuse GNSS measurements with MEMS Accelerometer/Gyroscope sensors in a loosely-coupled scheme. To support longer periods of GNSS outages, an advanced curve-to-curve map-matching algorithm using Hidden-Markov Models (HMM) is developed. Map-matched data points are used as position measurement feedback to the developed Kalman Filter. The developed map- aided fusion system was tested on real-road data collected in dense downtown area under long periods of GNSS service interruptions. The map-matching results showed 100 % accuracy under noisy GNSS. The results also showed robust localization performance under several minutes of GNSS blockage. The developed system is useful for autonomous cars navigation, LBS, and IoT localization in GNSS-denied areas

Infrastructure Wi-Fi for connected autonomous vehicle positioning : a review of the state-of-the-art

In order to realize intelligent vehicular transport networks and self driving cars, connected autonomous vehicles (CAVs) are required to be able to estimate their position to the nearest centimeter. Traditional positioning in CAVs is realized by using a global navigation satellite system (GNSS) such as global positioning system (GPS) or by fusing weighted location parameters from a GNSS with an inertial navigation systems (INSs). In urban environments where Wi-Fi coverage is ubiquitous and GNSS signals experience signal blockage, multipath or non line-of-sight (NLOS) propagation, enterprise or carrier-grade Wi-Fi networks can be opportunistically used for localization or “fused” with GNSS to improve the localization accuracy and precision. While GNSS-free localization systems are in the literature, a survey of vehicle localization from the perspective of a Wi-Fi anchor/infrastructure is limited. Consequently, this review seeks to investigate recent technological advances relating to positioning techniques between an ego vehicle and a vehicular network infrastructure. Also discussed in this paper is an analysis of the location accuracy, complexity and applicability of surveyed literature with respect to intelligent transportation system requirements for CAVs. It is envisaged that hybrid vehicular localization systems will enable pervasive localization services for CAVs as they travel through urban canyons, dense foliage or multi-story car parks

Survey on Recent Advances in Integrated GNSSs Towards Seamless Navigation Using Multi-Sensor Fusion Technology

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.©2021 The Authors. Published by ION.fi=vertaisarvioimaton|en=nonPeerReviewed

Lane-Precise Localization with Production Vehicle Sensors and Application to Augmented Reality Navigation

This works describes an approach to lane-precise localization on current digital maps. A particle filter fuses data from production vehicle sensors, such as GPS, radar, and camera. Performance evaluations on more than 200 km of data show that the proposed algorithm can reliably determine the current lane. Furthermore, a possible architecture for an intuitive route guidance system based on Augmented Reality is proposed together with a lane-change recommendation for unclear situations

Algorithms for Autonomous Personal Navigation Systems

Personal positioning is a challenging topic in the area of navigation mainly because of the cost, size and power consumption constraints imposed on the hardware. Satellite based positioning techniques can meet the requirements for many applications, but cover well only outdoor environment. Problems like weak satellite signals make the positioning impossible indoors. Urban canyons are also difficult areas for GNSS based navigation because of large multipath errors and satellite signal outages. Many applications require seamless positioning in all environments. However, there is no overall solution for navigation in GNSS denied environment, which is reliable, accurate, cost effective and quickly installed. Recently developed systems for indoor positioning often require pre-installed infrastructure. Another approach is to use fully autonomous navigation systems based on self-contained sensors and street or indoor maps. This thesis is concerned with autonomous personal navigation devices, which do not rely on the reception of external information, like satellite or terrestrial signals. The three proposed algorithms can be integrated into personal navigation systems. The first algorithm computes positioning for a map aided navigation system designed for land vehicles traveling on road network. The novelty is in application of particle filtering to vehicle navigation using road network database. The second algorithm is aimed at map aided vehicle navigation indoors. The novelty is in the method for correction of position and heading. The third algorithm computes solution for pedestrian navigation system, which is based on body mounted inertial measurement unit and models of human gait

Cooperative methods for vehicle localization

Abstract : Embedded intelligence in vehicular applications is becoming of great interest since the last two decades. Position estimation has been one of the most crucial pieces of information for Intelligent Transportation Systems (ITS). Real time, accurate and reliable localization of vehicles has become particularly important for the automotive industry. The significant growth of sensing, communication and computing capabilities over the recent years has opened new fields of applications, such as ADAS (Advanced driver assistance systems) and active safety systems, and has brought the ability of exchanging information between vehicles. Most of these applications can benefit from more accurate and reliable localization. With the recent emergence of multi-vehicular wireless communication capabilities, cooperative architectures have become an attractive alternative to solving the localization problem. The main goal of cooperative localization is to exploit different sources of information coming from different vehicles within a short range area, in order to enhance positioning system efficiency, while keeping the cost to a reasonable level. In this Thesis, we aim to propose new and effective methods to improve vehicle localization performance by using cooperative approaches. In order to reach this goal, three new methods for cooperative vehicle localization have been proposed and the performance of these methods has been analyzed. Our first proposed cooperative method is a Cooperative Map Matching (CMM) method which aims to estimate and compensate the common error component of the GPS positioning by using cooperative approach and exploiting the communication capability of the vehicles. Then we propose the concept of Dynamic base station DGPS (DDGPS) and use it to generate GPS pseudorange corrections and broadcast them for other vehicles. Finally we introduce a cooperative method for improving the GPS positioning by incorporating the GPS measured position of the vehicles and inter-vehicle distances. This method is a decentralized cooperative positioning method based on Bayesian approach. The detailed derivation of the equations and the simulation results of each algorithm are described in the designated chapters. In addition to it, the sensitivity of the methods to different parameters is also studied and discussed. Finally in order to validate the results of the simulations, experimental validation of the CMM method based on the experimental data captured by the test vehicles is performed and studied. The simulation and experimental results show that using cooperative approaches can significantly increase the performance of the positioning methods while keeping the cost to a reasonable amount.Résumé : L’intelligence embarquée dans les applications véhiculaires devient un grand intérêt depuis les deux dernières décennies. L’estimation de position a été l'une des parties les plus cruciales concernant les systèmes de transport intelligents (STI). La localisation précise et fiable en temps réel des véhicules est devenue particulièrement importante pour l'industrie automobile. Les améliorations technologiques significatives en matière de capteurs, de communication et de calcul embarqué au cours des dernières années ont ouvert de nouveaux champs d'applications, tels que les systèmes de sécurité active ou les ADAS, et a aussi apporté la possibilité d'échanger des informations entre les véhicules. Une localisation plus précise et fiable serait un bénéfice pour ces applications. Avec l'émergence récente des capacités de communication sans fil multi-véhicules, les architectures coopératives sont devenues une alternative intéressante pour résoudre le problème de localisation. L'objectif principal de la localisation coopérative est d'exploiter différentes sources d'information provenant de différents véhicules dans une zone de courte portée, afin d'améliorer l'efficacité du système de positionnement, tout en gardant le coût à un niveau raisonnable. Dans cette thèse, nous nous efforçons de proposer des méthodes nouvelles et efficaces pour améliorer les performances de localisation du véhicule en utilisant des approches coopératives. Afin d'atteindre cet objectif, trois nouvelles méthodes de localisation coopérative du véhicule ont été proposées et la performance de ces méthodes a été analysée. Notre première méthode coopérative est une méthode de correspondance cartographique coopérative (CMM, Cooperative Map Matching) qui vise à estimer et à compenser la composante d'erreur commune du positionnement GPS en utilisant une approche coopérative et en exploitant les capacités de communication des véhicules. Ensuite, nous proposons le concept de station de base Dynamique DGPS (DDGPS) et l'utilisons pour générer des corrections de pseudo-distance GPS et les diffuser aux autres véhicules. Enfin, nous présentons une méthode coopérative pour améliorer le positionnement GPS en utilisant à la fois les positions GPS des véhicules et les distances inter-véhiculaires mesurées. Ceci est une méthode de positionnement coopératif décentralisé basé sur une approche bayésienne. La description détaillée des équations et les résultats de simulation de chaque algorithme sont décrits dans les chapitres désignés. En plus de cela, la sensibilité des méthodes aux différents paramètres est également étudiée et discutée. Enfin, les résultats de simulations concernant la méthode CMM ont pu être validés à l’aide de données expérimentales enregistrées par des véhicules d'essai. La simulation et les résultats expérimentaux montrent que l'utilisation des approches coopératives peut augmenter de manière significative la performance des méthodes de positionnement tout en gardant le coût à un montant raisonnable

Automotive applications of high precision GNSS

This thesis aims to show that Global Navigation Satellite Systems (GNSS) positioning can play a significant role in the positioning systems of future automotive applications. This is through the adoption of state-of-the-art GNSS positioning technology and techniques, and the exploitation of the rapidly developing vehicle-to-vehicle concept. The merging together of these two developments creates greater performance than can be achieved separately. The original contribution of this thesis comes from this combination: Through the introduction of the Pseudo-VRS concept. Pseudo-VRS uses the princples of Network Real Time Kinematic (N-RTK) positioning to share GNSS information between vehicles, which enables absolute vehicle positioning. Pseudo-VRS is shown to improve the performance of high precision GNSS positioning for road vehicles, through the increased availability of GNSS correction messages and the rapid resolution of the N-RTK fixed solution. Positioning systems in the automotive sector are dominated by satellite-based solutions provided by GNSS. This has been the case since May 2001, when the United States Department of Defense switched off Selective Availability, enabling significantly improved positioning performance for civilian users. The average person most frequently encounters GNSS when using electronic personal navigation devices. The Sat Nav or GPS Navigator is ubiquitous in modern societies, where versions can be found on nomadic devices such as smartphones and dedicated personal navigation devices, or built in to the dashboards of vehicles. Such devices have been hugely successful due to their intrinsic ability to provide position information anywhere in the world with an accuracy of approximately 10 metres, which has proved ideal for general navigation applications. There are a few well known limitations of GNSS positioning, including anecdotal evidence of incorrect navigation advice for personal navigation devices, but these are minor compared to the overall positioning performance. Through steady development of GNSS positioning devices, including the integration of other low cost sensors (for instance, wheel speed or odometer sensors in vehicles), and the development of robust map matching algorithms, the performance of these devices for navigation applications is truly incredible. However, when tested for advanced automotive applications, the performance of GNSS positioning devices is found to be inadequate. In particular, in the most advanced fields of research such as autonomous vehicle technology, GNSS positioning devices are relegated to a secondary role, or often not used at all. They are replaced by terrestrial sensors that provide greater situational awareness, such as radar and lidar. This is due to the high performance demand of such applications, including high positioning accuracy (sub-decimetre), high availability and continuity of solutions (100%), and high integrity of the position information. Low-cost GNSS receivers generally do not meet such requirements. This could be considered an enormous oversight, as modern GNSS positioning technology and techniques have significantly improved satellite-based positioning performance. Other non-GNSS techniques also have their limitations that GNSS devices can minimise or eliminate. For instance, systems that rely on situational awareness require accurate digital maps of their surroundings as a reference. GNSS positioning can help to gather this data, provide an input, and act as a fail-safe in the event of digital map errors. It is apparent that in order to deliver advanced automotive applications - such as semi- or fully-autonomous vehicles - there must be an element of absolute positioning capability. Positioning systems will work alongside situational awareness systems to enable the autonomous vehicles to navigate through the real world. A strong candidate for the positioning system is GNSS positioning. This thesis builds on work already started by researchers at the University of Nottingham, to show that N-RTK positioning is one such technique. N-RTK can provide sub-decimetre accuracy absolute positioning solutions, with high availability, continuity, and integrity. A key component of N-RTK is the availability of real-time GNSS correction data. This is typically delivered to the GNSS receiver via mobile internet (for a roving receiver). This can be a significant limitation, as it relies on the performance of the mobile communications network, which can suffer from performance degradation during dynamic operation. Mobile communications systems are expected to improve significantly over the next few years, as consumers demand faster download speeds and wider availability. Mobile communications coverage already covers a high percentage of the population, but this does not translate into a high percentage of a country's geography. Pockets of poor coverage, often referred to as notspots, are widespread. Many of these notspots include the transportation infrastructure. The vehicle-to-vehicle concept has made significant forward steps in the last few years. Traditionally promoted as a key component of future automotive safety applications, it is now driven primarily by increased demand for in-vehicle infotainment. The concept, which shares similarities with the Internet of Things and Mobile Ad-hoc Networks, relies on communication between road vehicles and other road agents (such as pedestrians and road infrastructure). N-RTK positioning can take advantage of this communication link to minimise its own communications-related limitations. Sharing GNSS information between local GNSS receivers enables better performance of GNSS positioning, based on the principles of differential GNSS and N-RTK positioning techniques. This advanced concept is introduced and tested in this thesis. The Pseudo VRS concept follows the protocols and format of sharing GNSS data used in N-RTK positioning. The technique utilises the latest GNSS receiver design, including multiple frequency measurements and high quality antennas