Abstract

Fusion is a common topic nowadays in Advanced Driver Assistance Systems (ADAS). The demanding requirements of safety applications require trustable sensing technologies. Fusion allows to provide trustable detections by combining different sensor devices, fulfilling the requirements of safety applications. High level fusion scheme is presented; able to improve classic ADAS systems by combining different sensing technologies i.e. laser scanner and computer vision. By means of powerful Data Fusion (DF) algorithms, the performance of classic ADAS detection systems is enhanced. Fusion is performed in a decentralized scheme (high level), allowing scalability; hence new sensing technologies can easily be added to increase the trustability and the accuracy of the overall system. Present work focus in the Data Fusion scheme used to combine the information of the sensors at high level. Although for completeness some details of the different detection algorithms (low level) of the different sensors is provided. The proposed work adapts a powerful Data Association technique for Multiple Targets Tracking (MTT): Joint Probabilistic Data Association (JPDA) to improve the trustability of the ADAS detection systems. The final application provides real time detection of road users (pedestrians and vehicles) in real road situations. The tests performed proved the improvement of the use of Data Fusion algorithms. Furthermore, comparison with other classic algorithms such as Global Nearest Neighbors (GNN) proved the performance of the overall architecture. This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01) and (GRANT TRA 2011-29454-C03-02). CAM through SEGAUTO-II (S2009/DPI-1509) .

Abstract

The current work deals with the scenario of growing population in the cities and the associated sustainable mobility problems. To address this problem it is developed an system called INTEGRA for a mobile device, based on the integration from different data sources such as multi-modal public transportation systems, car and bike sharing. This system is able to show and give guidance towards points of interests (POI) and promote a social collaborative network for the share of notations towards POI classification. This work is a contribution for the European Project START, where we propose the Integra System. This system’s goal is to make travel easy and sustainable improving information systems integration. This goal is shared by transport operators and authorities associated to the Integra registered brand, promoted by the START consortium, that is already prepared for UK, France, Spain and Portugal.

Abstract

Electric Vehicles (EVs) are being introduced in the market, but batteries reduced energy storage capacity and the lack of a high density charging infrastructure limit their autonomy range. In order to overcome this limitation, we propose developing a new solution enabling drivers to drive longer distances. This will be achieved by integrating some components of the cooperative transport infrastructure (charging system, public transport system and the vehicle), and by increasing driving autonomy through energy consumption reduction obtained with the improvement of driving efficiency. In this work we show how to create a cooperative system in a mobile device to integrate public transportation real time information in an EV. Integration of EVs with public transport system allows extension of driving autonomy beyond the storing capacity of vehicle’s batteries. Supplying information on availability, schedule and price of public transport allows the driver to plan the journey using EV and public transportation in a complementary way, using functions as car parking booking (and charging) and ticket buying. This information is integrated in a mobile device providing the driver with a collaborative holistic approach of different public transportation infrastructure sources, that can be combined with real traffic information, parking places and charging slots and current driver position, to support the driver decision making process. FEDER Funds - Operational Programme for Competitiveness Factors (COMPETE) PTDC/EEA-EEL/104569/2008 and the project MITPT/ EDAM-SMS/0030/2008. Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EEA-EEL/104569/2008, MITPT/EDAM-SMS/0030/2008.

Abstract

Purpose Traffic congestion is a large-scale problem in urban areas all over the world that can lead to substantial costs for travellers and business operations. This paper focus on how to measure the way in which congestion selectively affects different traffic streams, with special emphasis on light duty vehicles travelling around a city. Methods The idea is to integrate a dataset collecting Global Positioning System (GPS) vehicle traces with road side data sources related to traffic conditions in a road network, which on the other hand usually lack focus on specific traffic streams. The core of the data integration method is the creation of a specific indicator focusing on the time lost in congestion. This is a Key Performance Indicator (KPI) of an urban network that is of paramount importance as a decision support tool for policy makers, also because it has an impact on other key issues such as air pollution, noise emissions, energy efficiency and health problems. Then, a method is proposed to quantify the congestion KPI in a highly disaggregated fashion (each single vehicle travelling on each single link or street segment). Results This KPI can be used to inform a wide range of policy actions within the transport sector, both from the viewpoint of a city and from that of an individual actor of the transport system, such as the operator of a fleet of vehicles for urban freight deliveries. Some preliminary examples of how the aggregation of the KPI at different scales can provide insights into the transport system are presented. Document type: Article

Abstract

One approach to continuously achieve a certain data quality level is to use an integration pipeline that continuously checks and monitors the quality of a data set according to defined metrics. This approach is inspired by Continuous Integration pipelines, that have been introduced in the area of software development and DevOps to perform continuous source code checks. By investigating in possible tools to use and discussing the specific requirements for RDF data sets, an integration pipeline is derived that joins current approaches of the areas of software-development and semantic-web as well as reuses existing tools. As these tools have not been built explicitly for CI usage, we evaluate their usability and propose possible workarounds and improvements. Furthermore, a real-world usage scenario is discussed, outlining the benefit of the usage of such a pipeline.

Abstract

Targeted information sources include radar and ADS (Automatic Dependent Surveillance) for civil ATM (Air Traffic Management) systems, and the new navigation system based on satellites has the capability of global coverage. In order to solve the surveillance problem in mid-and-high altitude airspace and approaching airspace, this paper proposes a filter-based covariance matrix weighting method, measurement variance weighting method, and measurement-first weighted fusion method weighting integration algorithm to improve the efficiency of data integration calculation under fixed accuracy. Besides this, this paper focuses on the technology of the integration of a multi-radar surveillance system and automated related surveillance system in the ATM system and analyzes the constructional method of a multigeneration surveillance data integration system, as well as establishing the targeted model of sensors and the target track and designing the logical structure of multi-radar and ADS data integration. Document type: Article