Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements

© 2016 Elsevier Ltd.There has been an increasing role played by Global Navigation Satellite Systems (GNSS) in Intelligent Transportation System (ITS) applications in recent decades. In particular, centimeter/decimetre positioning accuracy is required for some safety related applications, such as lane control, collision avoidance, and intelligent speed assistance. Lane-level Anomalous driving detection underpins these safety-related ITS applications. The two major issues associated with such detection are (1) accessing high accuracy vehicle positioning and dynamic parameters; and (2) extraction of irregular driving patterns from such information. This paper introduces a new integrated framework for detecting lane-level anomalous driving, by combining Global Positioning Systems (GPS), BeiDou, and Inertial Measurement Unit (IMU) with advanced algorithms. Specifically, we use Unscented Particle Filter (UPF) to perform data fusion with different positioning sources. The detection of different types of Anomalous driving is achieved based on the application of a Fuzzy Inference System (FIS) with a newly introduced velocity-based indicator. The framework proposed in this paper yield significantly improved accuracy in terms of positioning and Anomalous driving detection compared to state-of-the-art, while offering an economically viable solution for performing these tasks

Analysing the effects of sensor fusion, maps and trust models on autonomous vehicle satellite navigation positioning

This thesis analyzes the effects of maps, sensor fusion and trust models on autonomous vehicle satellite positioning. The aim is to analyze the localization improvements that commonly used sensors, technologies and techniques provide to autonomous vehicle positioning. This thesis includes both survey of localization techniques used by other research and their localization accuracy results as well as experimentation where the effects of different technologies and techniques on lateral position accuracy are reviewed. The requirements for safe autonomous driving are strict and while the performance of the average global navigation satellite system (GNSS) receiver alone may not prove to be adequate enough for accurate positioning, it may still provide valuable position data to an autonomous vehicle. For the vehicle, this position data may provide valuable information about the absolute position on the globe, it may improve localization accuracy through sensor fusion and it may act as an independent data source for sensor trust evaluation. Through empirical experimentation, the effects of sensor fusion and trust functions with an inertial measurement unit (IMU) on GNSS lateral position accuracy are measured and analyzed. The experimentation includes the measurements from both consumer-grade devices mounted on a traditional automobile and high-end devices of a truck that is capable of autonomous driving in a monitored environment. The maps and LIDAR measurements used in the experiments are prone to errors and are taken into account in the analysis of the data

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

SaPPART White paper. Better use of Global Navigation Satellite Systems for safer and greener transport

Transport and mobility services are crucial to the society that faces important challenges. Up to date, transport facilities and services have been fundamental to economic growth. However, there have significant and unacceptable negative impacts on the environment including pollution, noise and climate change. Therefore, it is paramount that the efficiency of the transport system is improved significantly including lower consumption of energy. A way of achieving this is through the concept of smart transport that exploits Intelligent Transport Systems (ITS) technology. ITS are built on three technology pillars: information, communication and positioning technologies. Of the three technologies, positioning could be argued to be the least familiar amongst transport stakeholders. However, a quick investigation reveals that there are a wide variety of transport and related services often associated with communication technologies that are supported by positioning. Currently, the positioning is provided in the majority of the cases by Global Navigation Satellite System (GNSS), among which the Global Positioning System (GPS) is the pioneer and still the most widely used system. The other current fully operational stand-alone system is Russia’s GLONASS. As these operational systems were not originally and specifically designed for transport applications, the actual capabilities and limitations of the current GNSS are not fully understood by many stakeholders. Therefore, better knowledge of these limitations and their resolution should enable a much more rapid deployment of ITS. This white paper is produced by the members of the COST Action SaPPART with two principal aims. The first is to explain the principles, state-of-the-art performance of GNSS technology and added value in the field of transport. The second aim is to deliver key messages to the stakeholders to facilitate the deployment of GNSS technology and thus contribute to the development of smarter and greener transport systems. The first chapter highlights the important role of positioning in today transport systems and the added value of accurate and reliable positioning for critical systems. The second chapter is about positioning technologies for transport: GNSS and their different aiding and augmentation methods are described, but the other complementary technologies are also introduced. The third and last chapter is about the management of performances inside a positioning-based intelligent transport system, between the positioning system itself and the application-specific part of the system which processes the raw position for delivering its service

Information Aided Navigation: A Review

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.Comment: 8 figures, 3 table

Use Of Smartphones for Ensuring Vulnerable Road User Safety through Path Prediction and Early Warning: An In-Depth Review of Capabilities, Limitations and Their Applications in Cooperative Intelligent Transport Systems

The field of cooperative intelligent transport systems and more specifically pedestrians to vehicles could be characterized as quite challenging, since there is a broad research area to be studied, with direct positive results to society. Pedestrians to vehicles is a type of cooperative intelligent transport system, within the group of early warning collision/safety system. In this article, we examine the research and applications carried out so far within the field of pedestrians to vehicles cooperative transport systems by leveraging the information coming from vulnerable road users’ smartphones. Moreover, an extensive literature review has been carried out in the fields of vulnerable road users outdoor localisation via smartphones and vulnerable road users next step/movement prediction, which are closely related to pedestrian to vehicle applications and research. We identify gaps that exist in these fields that could be improved/extended/enhanced or newly developed, while we address future research objectives and methodologies that could support the improvement/development of those identified gaps

GNSS Vulnerabilities and Existing Solutions:A Review of the Literature

This literature review paper focuses on existing vulnerabilities associated with global navigation satellite systems (GNSSs). With respect to the civilian/non encrypted GNSSs, they are employed for proving positioning, navigation and timing (PNT) solutions across a wide range of industries. Some of these include electric power grids, stock exchange systems, cellular communications, agriculture, unmanned aerial systems and intelligent transportation systems. In this survey paper, physical degradations, existing threats and solutions adopted in academia and industry are presented. In regards to GNSS threats, jamming and spoofing attacks as well as detection techniques adopted in the literature are surveyed and summarized. Also discussed are multipath propagation in GNSS and non line-of-sight (NLoS) detection techniques. The review also identifies and discusses open research areas and techniques which can be investigated for the purpose of enhancing the robustness of GNSS

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