Hybrid state estimation applications for joint traffic monitoring and incident detection

This dissertation is motivated by the practical problems of highway traffic estimation and incident detection using measurements from various sensor types. It proposes a framework to jointly estimate the traffic state and incidents in a hybrid state estimation problem where a continuous variable models the traffic state and a discrete model variable identifies the location and severity of an incident. Clearly, knowledge of an incident can improve post-incident traffic state estimates. Moreover, knowledge of the traffic state can be used to improve detection of incidents, by observing when the predicted traffic state differs significantly from the observed measurements. Two macroscopic traffic flow models are deployed to describe the evolution of traffic. Both the first order model and the second order model are extended to hybrid models by embedding a model parameter to denote the number of lanes open along the highway. The resulting traffic incident models are capable of describing traffic dynamics under both non-incident and incident scenarios that result in lane blockages. Next, several nonlinear filters are proposed to solve the joint traffic state estimation and incident detection problem. First, a multiple model particle filter and an interactive multiple model ensemble Kalman filter are proposed, where the particle filter or the ensemble Kalman filter are used to accommodate the nonlinearity of the traffic model, and multiple model methods are deployed to address the switching dynamics of traffic when incidents occur. Next, the multiple model particle filter is extended to a multiple model particle smoother to improve the estimation accuracy when data is limited. Finally, a variant of the multiple model particle filter, called the efficient multiple model particle filter, is developed for field implementations, which requires significantly less computation time compared to the other filters considered in this thesis. To validate the framework, the proposed nonlinear filters are implemented on the first order and second order traffic flow models, and tested in the microscopic traffic simulation software CORSIM and on field data collected on I-880 in California, which includes density measurements from inductive loops and speed measurements from GPS equipped vehicles. The results show that with either traffic flow model, the proposed traffic estimation algorithms are capable of jointly estimating the traffic state and detecting incidents when the traffic flow is high (i.e., when an incident results in congestion). The proposed algorithms are also compared with existing algorithms that independently estimate the traffic state or incidents. The results show that jointly estimating the state and incidents in one algorithm may perform better than two dedicated algorithms working independently, especially when loop detectors are sparse and the penetration rate of GPS equipped vehicles is high

A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

Architectures for embedded multimodal sensor data fusion systems in the robotics : and airport traffic suveillance ; domain

Smaller autonomous robots and embedded sensor data fusion systems often suffer from limited computational and hardware resources. Many ‘Real Time’ algorithms for multi modal sensor data fusion cannot be executed on such systems, at least not in real time and sometimes not at all, because of the computational and energy resources needed, resulting from the architecture of the computational hardware used in these systems. Alternative hardware architectures for generic tracking algorithms could provide a solution to overcome some of these limitations. For tracking and self localization sequential Bayesian filters, in particular particle filters, have been shown to be able to handle a range of tracking problems that could not be solved with other algorithms. But particle filters have some serious disadvantages when executed on serial computational architectures used in most systems. The potential increase in performance for particle filters is huge as many of the computational steps can be done concurrently. A generic hardware solution for particle filters can relieve the central processing unit from the computational load associated with the tracking task. The general topic of this research are hardware-software architectures for multi modal sensor data fusion in embedded systems in particular tracking, with the goal to develop a high performance computational architecture for embedded applications in robotics and airport traffic surveillance domain. The primary concern of the research is therefore: The integration of domain specific concept support into hardware architectures for low level multi modal sensor data fusion, in particular embedded systems for tracking with Bayesian filters; and a distributed hardware-software tracking systems for airport traffic surveillance and control systems. Runway Incursions are occurrences at an aerodrome involving the incorrect presence of an aircraft, vehicle, or person on the protected area of a surface designated for the landing and take-off of aircraft. The growing traffic volume kept runway incursions on the NTSB’s ‘Most Wanted’ list for safety improvements for over a decade. Recent incidents show that problem is still existent. Technological responses that have been deployed in significant numbers are ASDE-X and A-SMGCS. Although these technical responses are a significant improvement and reduce the frequency of runway incursions, some runway incursion scenarios are not optimally covered by these systems, detection of runway incursion events is not as fast as desired, and they are too expensive for all but the biggest airports. Local, short range sensors could be a solution to provide the necessary affordable surveillance accuracy for runway incursion prevention. In this context the following objectives shall be reached. 1) Show the feasibility of runway incursion prevention systems based on localized surveillance. 2) Develop a design for a local runway incursion alerting system. 3) Realize a prototype of the system design using the developed tracking hardware.Kleinere autonome Roboter und eingebettete Sensordatenfusionssysteme haben oft mit stark begrenzter Rechenkapazität und eingeschränkten Hardwareressourcen zu kämpfen. Viele Echtzeitalgorithmen für die Fusion von multimodalen Sensordaten können, bedingt durch den hohen Bedarf an Rechenkapazität und Energie, auf solchen Systemen überhaupt nicht ausgeführt werden, oder zu mindesten nicht in Echtzeit. Der hohe Bedarf an Energie und Rechenkapazität hat seine Ursache darin, dass die Architektur der ausführenden Hardware und der ausgeführte Algorithmus nicht aufeinander abgestimmt sind. Dies betrifft auch Algorithmen zu Spurverfolgung. Mit Hilfe von alternativen Hardwarearchitekturen für die generische Ausführung solcher Algorithmen könnten sich einige der typischerweise vorliegenden Einschränkungen überwinden lassen. Eine Reihe von Aufgaben, die sich mit anderen Spurverfolgungsalgorithmen nicht lösen lassen, lassen sich mit dem Teilchenfilter, einem Algorithmus aus der Familie der Bayesschen Filter lösen. Bei der Ausführung auf traditionellen Architekturen haben Teilchenfilter gegenüber anderen Algorithmen einen signifikanten Nachteil, allerdings ist hier ein großer Leistungszuwachs durch die nebenläufige Ausführung vieler Rechenschritte möglich. Eine generische Hardwarearchitektur für Teilchenfilter könnte deshalb die oben genannten Systeme stark entlasten. Das allgemeine Thema dieses Forschungsvorhabens sind Hardware-Software-Architekturen für die multimodale Sensordatenfusion auf eingebetteten Systemen - speziell für Aufgaben der Spurverfolgung, mit dem Ziel eine leistungsfähige Architektur für die Berechnung entsprechender Algorithmen auf eingebetteten Systemen zu entwickeln, die für Anwendungen in der Robotik und Verkehrsüberwachung auf Flughäfen geeignet ist. Das Augenmerk des Forschungsvorhabens liegt dabei auf der Integration von vom Einsatzgebiet abhängigen Konzepten in die Architektur von Systemen zur Spurverfolgung mit Bayeschen Filtern, sowie auf verteilten Hardware-Software Spurverfolgungssystemen zur Überwachung und Führung des Rollverkehrs auf Flughäfen. Eine „Runway Incursion“ (RI) ist ein Vorfall auf einem Flugplatz, bei dem ein Fahrzeug oder eine Person sich unerlaubt in einem Abschnitt der Start- bzw. Landebahn befindet, der einem Verkehrsteilnehmer zur Benutzung zugewiesen wurde. Der wachsende Flugverkehr hat dafür gesorgt, das RIs seit über einem Jahrzehnt auf der „Most Wanted“-Liste des NTSB für Verbesserungen der Sicherheit stehen. Jüngere Vorfälle zeigen, dass das Problem noch nicht behoben ist. Technologische Maßnahmen die in nennenswerter Zahl eingesetzt wurden sind das ASDE-X und das A-SMGCS. Obwohl diese Maßnahmen eine deutliche Verbesserung darstellen und die Zahl der RIs deutlich reduzieren, gibt es einige RISituationen die von diesen Systemen nicht optimal abgedeckt werden. Außerdem detektieren sie RIs ist nicht so schnell wie erwünscht und sind - außer für die größten Flughäfen - zu teuer. Lokale Sensoren mit kurzer Reichweite könnten eine Lösung sein um die für die zuverlässige Erkennung von RIs notwendige Präzision bei der Überwachung des Rollverkehrs zu erreichen. Vor diesem Hintergrund sollen die folgenden Ziele erreicht werden. 1) Die Machbarkeit eines Runway Incursion Vermeidungssystems, das auf lokalen Sensoren basiert, zeigen. 2) Einen umsetzbaren Entwurf für ein solches System entwickeln. 3) Einen Prototypen des Systems realisieren, das die oben gennannte Hardware zur Spurverfolgung einsetzt

Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation

This study proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described using a mode-dependent first-order autoregressive (AR) stochastic process. The parameters of the AR process take different values depending on the mode of traffic operation – free flowing, congested or faulty – making this a hybrid stochastic process. Mode switching occurs according to a first-order Markov chain. This study proposes an expectation-maximization (EM) technique for estimating the transition matrix of this Markovian mode process and the parameters of the AR models for each mode. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia. The model thus obtained is validated by using the smoothed inference algorithms and an online particle filter. The authors also develop an EM parameter estimation that, in combination with a time-window shift technique, can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator

Statistical modelling and analysis of traffic: a dynamic approach

In both developed and emerging-economies, major cities continue to experience increasing traffic congestion. To address this issue, complex Traffic Management Systems (TMS) are employed in recent years to help manage traffic. These systems fuse traffic-surveillance-related information from a variety of sensors deployed across traffic networks. A TMS requires real-time information to make effective control decisions and to deliver trustworthy information to users, such as travel time, congestion level, etc. There are three fundamental inputs required by TMS, namely, traffic volume, vehicular speed, and traffic density. Using conventional traffic loop detectors one can directly measure flow and velocity. However, traffic density is more difficult to measure. The situation becomes more difficult for multi-lane motorways due to drivers lane-change behaviour. This research investigates statistical modelling and analysis of traffic flow. It contributes to the literature of transportation and traffic management and research in several aspects. First, it takes into account lane-changes in traffic modelling through incorporating a Markov chain model to describe the drivers lane-change behaviour. Secondly, the lane change probabilities between two adjacent lanes are not assumed to be fixed but rather they depend on the current traffic condition. A discrete choice model is used to capture drivers lane choice behaviour. The drivers choice probabilities are modelled by several traffic-condition related attributes such as vehicle time headway, traffic density and speed. This results in a highly nonlinear state equation for traffic density. To address the issue of high nonlinearity of the state space model, the EKF and UKF is used to estimate the traffic density recursively. In addition, a new transformation approach has been proposed to transform the observation equation from a nonlinear form to a linear one so that the potential approximation in the EKF & UKF can be avoided. Numerical studies have been conducted to investigate the performance of the developed method. The proposed method outperformed the existing methods for traffic density estimation in simulation studies. Furthermore, it is shown that the computational cost for updating the estimate of traffic densities for a multi-lane motorway is kept at a minimum so that online applications are feasible in practice. Consequently the traffic densities can be monitored and the relevant information can be fed into the traffic management system of interest