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)

Integrity - A topic for photogrammetry?

Photogrammetric methods and sensors like LIDAR, RADAR and cameras are becoming more and more important for new applications like highly automatic driving, since they enable capturing relative information of the ego vehicle w.r.t its environment. Integrity measure the trust that we can put in the navigation information of a system. The concept of integrity was first developed for civil aviation and is linked to reliability concepts well known in geodesy and photogrammetry. Currently, the navigation community is discussing how to guarantee integrity for car navigation and multi-sensor systems. In this paper, we will give a short review on integrity concepts and on the current discussion of how to apply it to car navigation. We will discuss which role photogrammetry could play to solve the open issues in the integrity definition and monitoring for multi-sensor systems. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

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

Long-Term Localization for Self-Driving Cars

Long-term localization is hard due to changing conditions, while relative localization within time sequences is much easier. To achieve long-term localization in a sequential setting, such as, for self-driving cars, relative localization should be used to the fullest extent, whenever possible.This thesis presents solutions and insights both for long-term sequential visual localization, and localization using global navigational satellite systems (GNSS), that push us closer to the goal of accurate and reliable localization for self-driving cars. It addresses the question: How to achieve accurate and robust, yet cost-effective long-term localization for self-driving cars?Starting in this question, the thesis explores how existing sensor suites for advanced driver-assistance systems (ADAS) can be used most efficiently, and how landmarks in maps can be recognized and used for localization even after severe changes in appearance. The findings show that:* State-of-the-art ADAS sensors are insufficient to meet the requirements for localization of a self-driving car in less than ideal conditions.GNSS and visual localization are identified as areas to improve.\ua0* Highly accurate relative localization with no convergence delay is possible by using time relative GNSS observations with a single band receiver, and no base stations.\ua0* Sequential semantic localization is identified as a promising focus point for further research based on a benchmark study comparing state-of-the-art visual localization methods in challenging autonomous driving scenarios including day-to-night and seasonal changes.\ua0* A novel sequential semantic localization algorithm improves accuracy while significantly reducing map size compared to traditional methods based on matching of local image features.\ua0* Improvements for semantic segmentation in challenging conditions can be made efficiently by automatically generating pixel correspondences between images from a multitude of conditions and enforcing a consistency constraint during training.\ua0* A segmentation algorithm with automatically defined and more fine-grained classes improves localization performance.\ua0* The performance advantage seen in single image localization for modern local image features, when compared to traditional ones, is all but erased when considering sequential data with odometry, thus, encouraging to focus future research more on sequential localization, rather than pure single image localization

무인자율주행을 위한 도로 지도 생성 및 측위

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 서승우.This dissertation aims to present precise and cost-efficient mapping and localization algorithms for autonomous vehicles. Mapping and localization are ones of the key components in autonomous vehicles. The major concern for mapping and localization research is maximizing the accuracy and precision of the systems while minimizing the cost. For this goal, this dissertation proposes a road map generation system to create a precise and efficient lane-level road map, and a localization system based on the proposed road map and affordable sensors. In chapter 2, the road map generation system is presented. The road map generation system integrates a 3D LIDAR data and high-precision vehicle positioning system to acquire accurate road geometry data. Acquired road geometry data is represented as sets of piecewise polynomial curves in order to increase the storage efficiency and the usability. From extensive experiments using a real urban and highway road data, it is verified that the proposed road map generation system generates a road map that is accurate and more efficient than previous road maps in terms of the storage efficiency and usability. In chapter 3, the localization system is presented. The localization system targets an environment that the localization is difficult due to the lack of feature information for localization. The proposed system integrates the lane-level road map presented in chapter 2, and various low-cost sensors for accurate and cost-effective vehicle localization. A measurement ambiguity problem due to the use of low-cost sensors and poor feature information was presented, and a probabilistic measurement association-based particle filter is proposed to resolve the measurement ambiguity problem. Experimental results using a real highway road data is presented to verify the accuracy and reliability of the localization system. In chapter 4, an application of the accurate vehicle localization system is presented. It is demonstrated that sharing of accurate position information among vehicles can improve the traffic flow and suppress the traffic jam effectively. The effect of the position information sharing is evaluated based on numerical experiments. For this, a traffic model is proposed by extending conventional SOV traffic model. The numerical experiments show that the traffic flow is increased based on accurate vehicle localization and information sharing among vehicles.Chapter 1 Introduction 1 1.1 Background andMotivations 1 1.2 Contributions and Outline of the Dissertation 3 1.2.1 Generation of a Precise and Efficient Lane-Level Road Map 3 1.2.2 Accurate and Cost-Effective Vehicle Localization in Featureless Environments 4 1.2.3 An Application of Precise Vehicle Localization: Traffic Flow Enhancement Through the Sharing of Accurate Position Information Among Vehicles 4 Chapter 2 Generation of a Precise and Efficient Lane-Level Road Map 6 2.1 RelatedWorks 9 2.1.1 Acquisition of Road Geometry 11 2.1.2 Modeling of Road Geometry 13 2.2 Overall System Architecture 15 2.3 Road Geometry Data Acquisition and Processing 17 2.3.1 Data Acquisition 18 2.3.2 Data Processing 18 2.3.3 Outlier Problem 26 2.4 RoadModeling 27 2.4.1 Overview of the sequential approximation algorithm 29 2.4.2 Approximation Process 30 2.4.3 Curve Transition 35 2.4.4 Arc length parameterization 38 2.5 Experimental Validation 39 2.5.1 Experimental Setup 39 2.5.2 Data Acquisition and Processing 40 2.5.3 RoadModeling 42 2.6 Summary 49 Chapter 3 Accurate and Cost-Effective Vehicle Localization in Featureless Environments 51 3.1 RelatedWorks 53 3.2 SystemOverview 57 3.2.1 Test Vehicle and Sensor Configuration 57 3.2.2 Augmented RoadMap Data 57 3.2.3 Vehicle Localization SystemArchitecture 61 3.2.4 ProblemStatement 62 3.3 Particle filter-based Vehicle Localization Algorithm 63 3.3.1 Initialization 65 3.3.2 Time Update 66 3.3.3 Measurement Update 66 3.3.4 Integration 68 3.3.5 State Estimation 68 3.3.6 Resampling 69 3.4 Map-Image Measurement Update with Probabilistic Data Association 69 3.4.1 Lane Marking Extraction and Measurement Error Model 70 3.5 Experimental Validation 76 3.5.1 Experimental Environments 76 3.5.2 Localization Accuracy 77 3.5.3 Effect of the Probabilistic Measurement Association 79 3.5.4 Effect of theMeasurement ErrorModel 80 3.6 Summary 80 Chapter 4 An Application of Precise Vehicle Localization: Traffic Flow Enhancement Through the Sharing of Accurate Position Information Among Vehicles 82 4.1 Extended SOVModel 84 4.1.1 SOVModel 85 4.1.2 Extended SOVModel 89 4.2 Results and Discussions 91 4.3 Summary 93 Chapter 5 Conclusion 95 Bibliography 97 국문 초록 108Docto

Seamless Positioning and Navigation in Urban Environment

L'abstract è presente nell'allegato / the abstract is in the attachmen

Cooperative Relative Positioning for Vehicular Environments

Fahrerassistenzsysteme sind ein wesentlicher Baustein zur Steigerung der Sicherheit im Straßenverkehr. Vor allem sicherheitsrelevante Applikationen benötigen eine genaue Information über den Ort und der Geschwindigkeit der Fahrzeuge in der unmittelbaren Umgebung, um mögliche Gefahrensituationen vorherzusehen, den Fahrer zu warnen oder eigenständig einzugreifen. Repräsentative Beispiele für Assistenzsysteme, die auf eine genaue, kontinuierliche und zuverlässige Relativpositionierung anderer Verkehrsteilnehmer angewiesen sind, sind Notbremsassitenten, Spurwechselassitenten und Abstandsregeltempomate. Moderne Lösungsansätze benutzen Umfeldsensorik wie zum Beispiel Radar, Laser Scanner oder Kameras, um die Position benachbarter Fahrzeuge zu schätzen. Dieser Sensorsysteme gemeinsame Nachteile sind deren limitierte Erfassungsreichweite und die Notwendigkeit einer direkten und nicht blockierten Sichtlinie zum Nachbarfahrzeug. Kooperative Lösungen basierend auf einer Fahrzeug-zu-Fahrzeug Kommunikation können die eigene Wahrnehmungsreichweite erhöhen, in dem Positionsinformationen zwischen den Verkehrsteilnehmern ausgetauscht werden. In dieser Dissertation soll die Möglichkeit der kooperativen Relativpositionierung von Straßenfahrzeugen mittels Fahrzeug-zu-Fahrzeug Kommunikation auf ihre Genauigkeit, Kontinuität und Robustheit untersucht werden. Anstatt die in jedem Fahrzeug unabhängig ermittelte Position zu übertragen, werden in einem neuartigem Ansatz GNSS-Rohdaten, wie Pseudoranges und Doppler-Messungen, ausgetauscht. Dies hat den Vorteil, dass sich korrelierte Fehler in beiden Fahrzeugen potentiell herauskürzen. Dies wird in dieser Dissertation mathematisch untersucht, simulativ modelliert und experimentell verifiziert. Um die Zuverlässigkeit und Kontinuität auch in "gestörten" Umgebungen zu erhöhen, werden in einem Bayesischen Filter die GNSS-Rohdaten mit Inertialsensormessungen aus zwei Fahrzeugen fusioniert. Die Validierung des Sensorfusionsansatzes wurde im Rahmen dieser Dissertation in einem Verkehrs- sowie in einem GNSS-Simulator durchgeführt. Zur experimentellen Untersuchung wurden zwei Testfahrzeuge mit den verschiedenen Sensoren ausgestattet und Messungen in diversen Umgebungen gefahren. In dieser Arbeit wird gezeigt, dass auf Autobahnen, die Relativposition eines anderen Fahrzeugs mit einer Genauigkeit von unter einem Meter kontinuierlich geschätzt werden kann. Eine hohe Zuverlässigkeit in der longitudinalen und lateralen Richtung können erzielt werden und das System erweist 90% der Zeit eine Unsicherheit unter 2.5m. In ländlichen Umgebungen wächst die Unsicherheit in der relativen Position. Mit Hilfe der on-board Sensoren können Fehler bei der Fahrt durch Wälder und Dörfer korrekt gestützt werden. In städtischen Umgebungen werden die Limitierungen des Systems deutlich. Durch die erschwerte Schätzung der Fahrtrichtung des Ego-Fahrzeugs ist vor Allem die longitudinale Komponente der Relativen Position in städtischen Umgebungen stark verfälscht.Advanced driver assistance systems play an important role in increasing the safety on today's roads. The knowledge about the other vehicles' positions is a fundamental prerequisite for numerous safety critical applications, making it possible to foresee critical situations, warn the driver or autonomously intervene. Forward collision avoidance systems, lane change assistants or adaptive cruise control are examples of safety relevant applications that require an accurate, continuous and reliable relative position of surrounding vehicles. Currently, the positions of surrounding vehicles is estimated by measuring the distance with e.g. radar, laser scanners or camera systems. However, all these techniques have limitations in their perception range, as all of them can only detect objects in their line-of-sight. The limited perception range of today's vehicles can be extended in future by using cooperative approaches based on Vehicle-to-Vehicle (V2V) communication. In this thesis, the capabilities of cooperative relative positioning for vehicles will be assessed in terms of its accuracy, continuity and reliability. A novel approach where Global Navigation Satellite System (GNSS) raw data is exchanged between the vehicles is presented. Vehicles use GNSS pseudorange and Doppler measurements from surrounding vehicles to estimate the relative positioning vector in a cooperative way. In this thesis, this approach is shown to outperform the absolute position subtraction as it is able to effectively cancel out common errors to both GNSS receivers. This is modeled theoretically and demonstrated empirically using simulated signals from a GNSS constellation simulator. In order to cope with GNSS outages and to have a sufficiently good relative position estimate even in strong multipath environments, a sensor fusion approach is proposed. In addition to the GNSS raw data, inertial measurements from speedometers, accelerometers and turn rate sensors from each vehicle are exchanged over V2V communication links. A Bayesian approach is applied to consider the uncertainties inherently to each of the information sources. In a dynamic Bayesian network, the temporal relationship of the relative position estimate is predicted by using relative vehicle movement models. Also real world measurements in highway, rural and urban scenarios are performed in the scope of this work to demonstrate the performance of the cooperative relative positioning approach based on sensor fusion. The results show that the relative position of another vehicle towards the ego vehicle can be estimated with sub-meter accuracy in highway scenarios. Here, good reliability and 90% availability with an uncertainty of less than 2.5m is achieved. In rural environments, drives through forests and towns are correctly bridged with the support of on-board sensors. In an urban environment, the difficult estimation of the ego vehicle heading has a mayor impact in the relative position estimate, yielding large errors in its longitudinal component

Reliable localization methods for intelligent vehicles based on environment perception

Mención Internacional en el título de doctorIn the near past, we would see autonomous vehicles and Intelligent Transport Systems (ITS) as a potential future of transportation. Today, thanks to all the technological advances in recent years, the feasibility of such systems is no longer a question. Some of these autonomous driving technologies are already sharing our roads, and even commercial vehicles are including more Advanced Driver-Assistance Systems (ADAS) over the years. As a result, transportation is becoming more efficient and the roads are considerably safer. One of the fundamental pillars of an autonomous system is self-localization. An accurate and reliable estimation of the vehicle’s pose in the world is essential to navigation. Within the context of outdoor vehicles, the Global Navigation Satellite System (GNSS) is the predominant localization system. However, these systems are far from perfect, and their performance is degraded in environments with limited satellite visibility. Additionally, their dependence on the environment can make them unreliable if it were to change. Accordingly, the goal of this thesis is to exploit the perception of the environment to enhance localization systems in intelligent vehicles, with special attention to their reliability. To this end, this thesis presents several contributions: First, a study on exploiting 3D semantic information in LiDAR odometry is presented, providing interesting insights regarding the contribution to the odometry output of each type of element in the scene. The experimental results have been obtained using a public dataset and validated on a real-world platform. Second, a method to estimate the localization error using landmark detections is proposed, which is later on exploited by a landmark placement optimization algorithm. This method, which has been validated in a simulation environment, is able to determine a set of landmarks so the localization error never exceeds a predefined limit. Finally, a cooperative localization algorithm based on a Genetic Particle Filter is proposed to utilize vehicle detections in order to enhance the estimation provided by GNSS systems. Multiple experiments are carried out in different simulation environments to validate the proposed method.En un pasado no muy lejano, los vehículos autónomos y los Sistemas Inteligentes del Transporte (ITS) se veían como un futuro para el transporte con gran potencial. Hoy, gracias a todos los avances tecnológicos de los últimos años, la viabilidad de estos sistemas ha dejado de ser una incógnita. Algunas de estas tecnologías de conducción autónoma ya están compartiendo nuestras carreteras, e incluso los vehículos comerciales cada vez incluyen más Sistemas Avanzados de Asistencia a la Conducción (ADAS) con el paso de los años. Como resultado, el transporte es cada vez más eficiente y las carreteras son considerablemente más seguras. Uno de los pilares fundamentales de un sistema autónomo es la autolocalización. Una estimación precisa y fiable de la posición del vehículo en el mundo es esencial para la navegación. En el contexto de los vehículos circulando en exteriores, el Sistema Global de Navegación por Satélite (GNSS) es el sistema de localización predominante. Sin embargo, estos sistemas están lejos de ser perfectos, y su rendimiento se degrada en entornos donde la visibilidad de los satélites es limitada. Además, los cambios en el entorno pueden provocar cambios en la estimación, lo que los hace poco fiables en ciertas situaciones. Por ello, el objetivo de esta tesis es utilizar la percepción del entorno para mejorar los sistemas de localización en vehículos inteligentes, con una especial atención a la fiabilidad de estos sistemas. Para ello, esta tesis presenta varias aportaciones: En primer lugar, se presenta un estudio sobre cómo aprovechar la información semántica 3D en la odometría LiDAR, generando una base de conocimiento sobre la contribución de cada tipo de elemento del entorno a la salida de la odometría. Los resultados experimentales se han obtenido utilizando una base de datos pública y se han validado en una plataforma de conducción del mundo real. En segundo lugar, se propone un método para estimar el error de localización utilizando detecciones de puntos de referencia, que posteriormente es explotado por un algoritmo de optimización de posicionamiento de puntos de referencia. Este método, que ha sido validado en un entorno de simulación, es capaz de determinar un conjunto de puntos de referencia para el cual el error de localización nunca supere un límite previamente fijado. Por último, se propone un algoritmo de localización cooperativa basado en un Filtro Genético de Partículas para utilizar las detecciones de vehículos con el fin de mejorar la estimación proporcionada por los sistemas GNSS. El método propuesto ha sido validado mediante múltiples experimentos en diferentes entornos de simulación.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridSecretario: Joshué Manuel Pérez Rastelli.- Secretario: Jorge Villagrá Serrano.- Vocal: Enrique David Martí Muño

Advances in Automated Driving Systems

Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic