Deploying spatial-stream query answering in C-ITS scenarios1

Semantic Web ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 41-77
Author(s):  
Thomas Eiter ◽  
Ryutaro Ichise ◽  
Josiane Xavier Parreira ◽  
Patrik Schneider ◽  
Lihua Zhao

Cooperative Intelligent Transport Systems (C-ITS) play an important role for providing the means to collect and exchange spatio-temporal data via V2X-based communication between vehicles and the infrastructure, which will become a central enabler for road safety of (semi)-autonomous vehicles. The Local Dynamic Map (LDM) is a key concept for integrating static and streamed data in a spatial context. The LDM has been semantically enhanced to allow for an elaborate domain model that is captured by a mobility ontology, and for queries over data streams that cater for semantic concepts and spatial relationships. Our approach for semantic enhancement is in the context of ontology-mediated query answering (OQA) and features conjunctive queries over DL-LiteA ontologies that support window operators over streams and spatial relations between spatial objects. In this paper, we show how this approach can be extended to address a wider range of use cases in the three C-ITS scenarios traffic statistics, traffic events detection, and advanced driving assistance systems. We define for the mentioned use cases requirements derived from necessary domain-specific features and report, based on them, on extensions of our query language and ontology model. The extensions include temporal relations, numeric predictions and trajectory predictions as well as optimization strategies such as caching. An experimental evaluation of queries that reflect the requirements has been conducted using the real-world traffic simulation tool PTV Vissim. It provides evidence for the feasibility/efficiency of our approach in the new scenarios.

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3783
Author(s):  
Sumbal Malik ◽  
Manzoor Ahmed Khan ◽  
Hesham El-Sayed

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Peter Baumann ◽  
Dimitar Misev ◽  
Vlad Merticariu ◽  
Bang Pham Huu

AbstractMulti-dimensional arrays (also known as raster data or gridded data) play a key role in many, if not all science and engineering domains where they typically represent spatio-temporal sensor, image, simulation output, or statistics “datacubes”. As classic database technology does not support arrays adequately, such data today are maintained mostly in silo solutions, with architectures that tend to erode and not keep up with the increasing requirements on performance and service quality. Array Database systems attempt to close this gap by providing declarative query support for flexible ad-hoc analytics on large n-D arrays, similar to what SQL offers on set-oriented data, XQuery on hierarchical data, and SPARQL and CIPHER on graph data. Today, Petascale Array Database installations exist, employing massive parallelism and distributed processing. Hence, questions arise about technology and standards available, usability, and overall maturity. Several papers have compared models and formalisms, and benchmarks have been undertaken as well, typically comparing two systems against each other. While each of these represent valuable research to the best of our knowledge there is no comprehensive survey combining model, query language, architecture, and practical usability, and performance aspects. The size of this comparison differentiates our study as well with 19 systems compared, four benchmarked to an extent and depth clearly exceeding previous papers in the field; for example, subsetting tests were designed in a way that systems cannot be tuned to specifically these queries. It is hoped that this gives a representative overview to all who want to immerse into the field as well as a clear guidance to those who need to choose the best suited datacube tool for their application. This article presents results of the Research Data Alliance (RDA) Array Database Assessment Working Group (ADA:WG), a subgroup of the Big Data Interest Group. It has elicited the state of the art in Array Databases, technically supported by IEEE GRSS and CODATA Germany, to answer the question: how can data scientists and engineers benefit from Array Database technology? As it turns out, Array Databases can offer significant advantages in terms of flexibility, functionality, extensibility, as well as performance and scalability—in total, the database approach of offering “datacubes” analysis-ready heralds a new level of service quality. Investigation shows that there is a lively ecosystem of technology with increasing uptake, and proven array analytics standards are in place. Consequently, such approaches have to be considered a serious option for datacube services in science, engineering and beyond. Tools, though, vary greatly in functionality and performance as it turns out.


Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3871
Author(s):  
Jiri Pokorny ◽  
Khanh Ma ◽  
Salwa Saafi ◽  
Jakub Frolka ◽  
Jose Villa ◽  
...  

Automated systems have been seamlessly integrated into several industries as part of their industrial automation processes. Employing automated systems, such as autonomous vehicles, allows industries to increase productivity, benefit from a wide range of technologies and capabilities, and improve workplace safety. So far, most of the existing systems consider utilizing one type of autonomous vehicle. In this work, we propose a collaboration of different types of unmanned vehicles in maritime offshore scenarios. Providing high capacity, extended coverage, and better quality of services, autonomous collaborative systems can enable emerging maritime use cases, such as remote monitoring and navigation assistance. Motivated by these potential benefits, we propose the deployment of an Unmanned Surface Vehicle (USV) and an Unmanned Aerial Vehicle (UAV) in an autonomous collaborative communication system. Specifically, we design high-speed, directional communication links between a terrestrial control station and the two unmanned vehicles. Using measurement and simulation results, we evaluate the performance of the designed links in different communication scenarios and we show the benefits of employing multiple autonomous vehicles in the proposed communication system.


Author(s):  
P. V. Kuper ◽  
M. Breunig ◽  
M. Al-Doori ◽  
A. Thomsen

Many of today´s world wide challenges such as climate change, water supply and transport systems in cities or movements of crowds need spatio-temporal data to be examined in detail. Thus the number of examinations in 3D space dealing with geospatial objects moving in space and time or even changing their shapes in time will rapidly increase in the future. Prominent spatio-temporal applications are subsurface reservoir modeling, water supply after seawater desalination and the development of transport systems in mega cities. All of these applications generate large spatio-temporal data sets. However, the modeling, management and analysis of 3D geo-objects with changing shape and attributes in time still is a challenge for geospatial database architectures. In this article we describe the application of concepts for the modeling, management and analysis of 2.5D and 3D spatial plus 1D temporal objects implemented in DB4GeO, our service-oriented geospatial database architecture. An example application with spatio-temporal data of a landfill, near the city of Osnabrück in Germany demonstrates the usage of the concepts. Finally, an outlook on our future research focusing on new applications with big data analysis in three spatial plus one temporal dimension in the United Arab Emirates, especially the Dubai area, is given.


2021 ◽  
Vol 13 (20) ◽  
pp. 11372
Author(s):  
Gemma Dolores Molero ◽  
Sara Poveda-Reyes ◽  
Ashwani Kumar Malviya ◽  
Elena García-Jiménez ◽  
Maria Chiara Leva ◽  
...  

Previous studies have highlighted inequalities and gender differences in the transport system. Some factors or fairness characteristics (FCs) strongly influence gender fairness in the transport system. The difference with previous studies, which focus on general concepts, is the incorporation of level 3 FCs, which are more detailed aspects or measures that can be implemented by companies or infrastructure managers and operators in order to increase fairness and inclusion in each use case. The aim of this paper is to find computational solutions, Bayesian networks, and analytic hierarchy processes capable of hierarchizing level 3 FCs and to predict by simulation their values in the case of applying some improvements. This methodology was applied to data from women in four use cases: railway transport, autonomous vehicles, bicycle sharing stations, and transport employment. The results showed that fairer railway transport requires increased personal space, hospitality rooms, help points, and helpline numbers. For autonomous vehicles, the perception of safety, security, and sustainability should be increased. The priorities for bicycle sharing stations are safer cycling paths avoiding hilly terrains and introducing electric bicycles, child seats, or trailers to carry cargo. In transport employment, the priorities are fair recruitment and promotion processes and the development of family-friendly policies.


Author(s):  
Andreea Sabau

In order to represent spatio-temporal data, many conceptual models have been designed and a part of them have been implemented. This chapter describes an approach of the conceptual modeling of spatio-temporal data, called 3SST. Also, the spatio-temporal conceptual and relational data models obtained by following the proposed phases are presented. The 3SST data model is obtained by following three steps: the construction of an entity-relationship spatio-temporal model, the specification of the domain model and the design of a class diagram which includes the objects characteristic to a spatiotemporal application and other needed elements. The relational model of the 3SST conceptual model is the implementation of the conceptual 3SST data model on a relational database platform. Both models are characterized by generality in representing spatial, temporal and spatio-temporal data. The spatial objects can be represented as points or objects with shape and the evolution of the spatio-temporal objects can be implemented as discrete or continuous in time, on time instants or time intervals. More than that, different types of spatial, temporal, spatio-temporal and event-based queries can be performed on represented data. Therefore, the proposed 3SST relational model can be considered the core of a spatio-temporal data model.


2020 ◽  
Vol 5 (10) ◽  
pp. 88
Author(s):  
Salvatore Trubia ◽  
Alessandro Severino ◽  
Salvatore Curto ◽  
Fabio Arena ◽  
Giovanni Pau

The goal of civil engineering has always been the research and implementation of methods, technologies, and infrastructures to improve the community’s quality of life. One of the branches of civil engineering that has the strongest effect on progress is transport. The quality of transport has a profound economic and social impact on our communities regarding trade (freight transport) and city livability (public transport systems). However, innovation is not the only way to improve the features above-mentioned, especially public transport, considering that it is usually beneficial to enhance and repurpose vehicles with appropriate adjustments to offer more efficient services. Other perspectives that influence public transport systems are the costs and times of design and construction, maintenance, operating costs, and environmental impact, especially concerning CO2 emissions. Considering these issues, among the various types of existing public transport systems, those of the so-called Bus Rapid Transit (BRT) offer worthwhile results. The BRT system is a type of public road transport operated by bus on reserved lanes, and it is significantly profitable, especially from an economic point of view, in areas where there are existing bus routes. Nonetheless, for the construction of works minimization, it is closely linked to other features that improve its usefulness, depending on the vehicles’ quality such as capacity, but above all, the propulsion or driving autonomy that would guarantee high efficiency. This paper introduces an analysis of some BRT systems operating worldwide, presenting the background, general technical features, and the correlation with autonomous vehicles.


2019 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
Umair Hasan ◽  
Andrew Whyte ◽  
Hamad Al Jassmi

Mobility is experiencing a revolution, as advanced communications, computers with big data capacities, efficient networks of sensors, and signals, are developing value-added applications such as intelligent spaces and autonomous vehicles. Another new technology that is both promising and might even be pervasive for faster, safer and more environmentally-friendly public transport (PT) is the development of autonomous vehicles (AVs). This study aims to understand the state of the current research on the artificially intelligent transportation system (ITS) and AVs through a critical evaluation of peer-reviewed literature. This study’s findings revealed that the majority of existing research (around 82% of studies) focused on AVs. Results show that AVs can potentially reduce more than 80% of pollutant emissions per mile if powered by alternate energy resources (e.g., natural gas, biofuel, electricity, hydrogen cells, etc.). Not only can private vehicle ownership be cut down by bringing in ridesharing but the average vehicle miles travelled (VMT) should also be reduced through improved PT. The main benefits of AV adoption were reported in the literature to be travel time, traffic congestion, cost and environmental factors. Findings revealed barriers such as technological uncertainties, lack of regulation, unawareness among stakeholders and privacy and security concerns, along with the fact that lack of simulation and empirical modelling data from pilot studies limit the application. AV–PT was also found to be the most sustainable strategy in dense urban areas to shift the heavy trip load from private vehicles.


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