Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells

Significant efforts have been made and are still being made on short-term traffic prediction methods, specially for highway traffic based on punctual measurements. Literature on predicting the spatial distribution of the traffic in urban intersections is, however, very limited. This work presents a novel data-driven prediction algorithm based on Random Forests regression over spatio-temporal aggregated data of vehicle counts inside a grid. The proposed approach aims to estimate future distribution of V2X traffic demand, providing a valuable input for a dynamic management of radio resources in small cells. Radio Access Networks (RAN) working in the terahertz band and deployed in small cells are expected to meet the high-demanding data rate requirements of connected vehicles. However, terahertz frequency propagation has important limitations in outdoor scenarios, including distance propagation, high absorption coefficients values and low reflection properties. More concretely, in settings such as complex road intersections, dynamic signal blockage and shadowing effects may cause significant power losses and compromise the quality of service for some vehicles. The forthcoming network demand, estimated from the regression algorithm is used to compute the losses expected due to other vehicles potentially located between the transmitter and the receiver. We conclude that our approach, which is designed from a grid-like perspective, outperforms other traffic prediction methods and the combined result of these predictions with a dynamic reflector orientation algorithm, as a use case application, allows reducing the ratio of vehicles that do not receive a minimum signal power

CogITS: Cognition-enabled network management for 5G V2X Communication

The 5G promise for ubiquitous communications is expected to be a key enabler for transportation efficiency. However, the consequent increase of both data payload and number of users derived from new Intelligent Transport Systems makes network management even more challenging; an ideal network management will need to be capable of self-managing fast moving nodes that sit in the 5G data plane. Platooning applications, for instance, need a highly flexible and high efficient infrastructure for optimal road capacity. Network management solutions have, then, to accommodate more intelligence in its decision-making process due to the network complexity of ITS. This paper proposes this envisioned architecture namely Cognition-enabled network management for 5G V2X Communication (CogITS). It is empowered by machine learning to dynamically allocate resources in the network based on traffic prediction and adaptable physical layer settings. Preliminary proof-of-concept validation results, in a platooning scenario, show that the proposed architecture can improve the overall network latency over time with a minimum increase of control message traffic

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

5G beyond 3GPP release 15 for connected automated mobility in cross-border contexts

Fifth-generation (5G) mobile networks aim to be qualified as the core connectivity infrastructures to address connected automated mobility (CAM), both from a technological and from a business perspective, for the higher automation levels defined by the automotive industry. Specifically, in some territories such as the European Union the cross-border corridors have relevance, as they are the cohesive paths for terrestrial transport. Therefore, 5G for CAM applications is planned to be deployed there first. However, cross-border contexts imply paramount communication challenges, such as seamless roaming, not addressed by current technology. This paper identifies relevant future 5G enhancements, specifically those specified by Third-Generation Partnership Project (3GPP) releases beyond Release 15, and outlines how they will support the ambitions of highly automated driving in cross-border corridors. In order to conduct this study, a set of representative use cases and the related communication requirements were identified. Then, for each use case, the most relevant 5G features were proposed. Some open issues are described at the end.Peer reviewe

Vision-Enhanced Low-Cost Localization In Crowdsourced Maps

The lane-level localization of vehicles with low-cost sensors is a challenging task. In situations in which Global Navigation Satellite Systems (GNSSs) suffer from weak observation geometry or from the influence of reflected signals, the fusion of heterogeneous information presents a suitable approach for improving the localization accuracy. We propose a solution based on a monocular front-facing camera, a low-cost inertial measurement unit (IMU), and a single-frequency GNSS receiver. The sensor data fusion is implemented as a tightly coupled Kalman filter that corrects the IMU-based trajectory with GNSS observations while employing European Geostationary Overlay Service correction data. Further, we consider vision-based complementary data that serve as an additional source of information. In contrast to other approaches, the camera is not used to infer the motion of the vehicle, but rather for directly correcting the localization results under the usage of map information. More specifically, the so-called camera-to-map alignment is done by comparing virtual 3D views (candidates) created from projected map data with lane geometry features that are extracted from the camera image. One strength of the proposed solution is its compatibility with state-of-the-art map data, which are publicly available from different sources. We validate the approach on real-world data recorded in The Netherlands and show that it presents a promising and cost-efficient means to support future advanced driver assistance systems

Connected and Automated Mobility Services in 5G Cross-Border Environments: Challenges and Prospects

The next generation of mobile networks, namely 5G, promises significant qualitative and quantitative advances for multiple vertical domains. However, most studies and investigations assess these advances under the implicit assumption of a single network service provider, with typical national coverage. In this article, we take a close look at the automotive sector and highlight a series of challenges emerging in the context of its inherent (inter)national mobility and the corresponding importance of cross-border and/or multioperator environments. Our target is to pinpoint the key influential factors affecting the transition toward seamless (cooperative) connected and automated mobility services within and across national borders. To this end, we identify and analyze a series of challenges in the areas of, networking, application, security, and regulation. We further present and discuss a series of corresponding solutions investigated in the pragmatic context of our experimental activities