Abstract

Since the use of systems of satellite positioning such as the global positioning system (GPS), applications have tried to locate vehicles on maps representing the environment with their attributes. For one decade, this has led to both localization and navigation services for users. Recently, new researches have begun in order to extend the functionalities of the existing systems and thus to develop new applications using these technologies in the design of driver assistance systems. These new systems will indeed allow us to anticipate road departures or prevent overspeed turn approaches. Nevertheless, to deploy such new functionalities, it is imperative to ensure the association of vehicle position with one of the roadmap segments. In this article, we propose a new approach based on the belief theory taking into account the imperfections of available data in order to ensure the positioning and tracking of a vehicle on a roadmap and to manage conflicts and ambiguities using a multi-hypotheses decision.

Abstract

mospheric turbulence poses a significant hazard to aviation, with severe encounters costing airlines millions of dollars per year in compensation, aircraft damage, and delays due to required post-event inspections and repairs. Moreover, attempts to avoid turbulent airspace cause flight delays and en route deviations that increase air traffic controller workload, disrupt schedules of air crews and passengers and use extra fuel. For these reasons, the Federal Aviation Administration and the National Aeronautics and Space Administration have funded the development of automated turbulence detection, diagnosis and forecasting products. This paper describes a methodology for fusing data from diverse sources and producing a real-time diagnosis of turbulence associated with thunderstorms, a significant cause of weather delays and turbulence encounters that is not well-addressed by current turbulence forecasts. The data fusion algorithm is trained using a retrospective dataset that includes objective turbulence reports from commercial aircraft and collocated predictor data. It is evaluated on an independent test set using several performance metrics including receiver operating characteristic curves, which are used for FAA turbulence product evaluations prior to their deployment. A prototype implementation fuses data from Doppler radar, geostationary satellites, a lightning detection network and a numerical weather prediction model to produce deterministic and probabilistic turbulence assessments suitable for use by air traffic managers, dispatchers and pilots. The algorithm is scheduled to be operationally implemented at the National Weather Service's Aviation Weather Center in 2014. Document type: Article

Abstract

Climate change is a major environmental threat of our time. Cities have a significant impact on greenhouse gas emissions as most of the traffic, industry, commerce and more than 50% of world population is situated in urban areas. Southern Europe is a region that faces financial turmoil, enhanced migratory fluxes and climate change pressure. The case study of Thessaloniki is presented, one of the only two cities in Greece with established climate change action plans. The effects of feasible traffic policies in year 2020 are assessed and their potential health impact is compared to a business as usual scenario. Two types of measures are investigated: operation of underground rail in the city centre and changes in fleet composition. Potential co-benefits from reduced greenhouse gas emissions on public health by the year 2020 are computed utilizing state-of-the-art concentration response functions for PM x , NO 2 and C 6 H 6 . Results show significant environmental health and monetary co-benefits when the city metro is coupled with appropriate changes in the traffic composition. Monetary savings due to avoided mortality or leukaemia incidence corresponding to the reduction in PM 10 , PM 2.5, NO 2 and C 6 H 6 exposure will be 56.6, 45, 37.7 and 1.0 million Euros respectively. Promotion of ‘green’ transportation in the city (i.e. the wide use of electric vehicles), will provide monetary savings from the reduction in PM 10 , PM 2.5 , NO 2 and C 6 H 6 exposure up to 60.4, 49.1, 41.2 and 1.08 million Euros. Overall, it was shown that the respective GHG emission reduction policies resulted in clear co-benefits in terms of air quality improvement, public health protection and monetary loss mitigation. Document type: Article

Abstract

This paper proposes a methodology for leak detection and isolation (LDI) in pipelines based on data fusion from two approaches: a steady-state estimation and an Extended Kalman Filter (EKF). The proposed method considers only pressure head and flow rate measurements at the pipeline ends, which contain intrinsic sensor and process noise. The LDI system is tested in real-time by using an USB data acquisition device that is implemented in MATLAB environment. The effectiveness of the method is analyzed by considering: online detection, location as well as quantification of non-concurrent leaks at different positions. The leak estimation error average is less than 1% of the flow rate and less than 3% in the leakage position. Furthermore, the incorporation of a steady-state estimation shows that the solution of the LDI problem has improved significantly with respect to the one that only considers the EKF estimation. An experimental analysis was also performed on the effectiveness of the proposed approach for different sampling rates and for different leakage positions Peer Reviewed

Abstract

SFINAG – the Austrian motorway operator – calculates a comprehensive traffic situation, travel time and travel"br" time forecast for the entire motorway and expressway network in Austria. Over the last few years, the nature of"br" travel time estimation has evolved rapidly. In addi tion to the ever-increasing number of detection technologies"br" (Bluetooth, WLAN, CEN DSRC, radar, ultrasound as well as FCD), the different qualities and quantities of"br" generated data make a more exact and near-time travel time calculation more complex. The goal of the ARMS"br" project is, to harmonize these data sources and to combine them in the best possible way, so that a comprehensive"br" travel time estimation is possible on the entire 2200 km-wide motorway and expressway network. This is achieved"br" on the one hand by intelligent data fusing. On the other hand, data of the ASFINAG event management, roadworks"br" management and a short-term forecast are also included, in order to be able to provide travel times on motorway"br" sections as quickly as possible for customers. By harmonizing the input data, different data sources can be"br" interlinked very well, but can also be dynamically expanded by new detection technologies. In addition to"br" optimizing the travel time calculation for car and truck traffic, local events leading to traffic restrictions or delays"br" can also be precisely located and categorized, even down to the exact start and end of congestion. Because this"br" data is fed back into the ASFINAG event management, the quality of traffic information as well as traffic"br" management is also significantly improved.

Abstract

International audience; In this work, we investigate the use of OpenStreetMap data for semantic labeling of Earth Observation images. Deep neural networks have been used in the past for remote sensing data classification from various sensors, including multispectral, hyperspectral, SAR and LiDAR data. While OpenStreetMap has already been used as ground truth data for training such networks, this abundant data source remains rarely exploited as an input information layer. In this paper, we study different use cases and deep network architectures to leverage OpenStreetMap data for semantic labeling of aerial and satellite images. Especially , we look into fusion based architectures and coarse-to-fine segmentation to include the OpenStreetMap layer into multispectral-based deep fully convolutional networks. We illustrate how these methods can be successfully used on two public datasets: ISPRS Potsdam and DFC2017. We show that OpenStreetMap data can efficiently be integrated into the vision-based deep learning models and that it significantly improves both the accuracy performance and the convergence speed of the networks.

Abstract

Nowadays, vehicle safety is constantly increasing thanks to the improvement of vehicle passive and active safety. However, on a daily usage of the car, traffic jams remains a problem. With limited space for road infrastructure, automation of the driving task on specific situation seems to be a possible solution. The French project ABV, which stands for low speed automation, tries to demonstrate the feasibility of the concept and to prove the benefits. In this article, we describe the scientific background of the project and expected outputs. Comment: TRA (2012) Document type: Conference object

Abstract

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks. Document type: Article