Object-level fusion for surround environment perception in automated driving applications

Driver assistance systems have increasingly relied on more sensors for new functions. As advanced driver assistance system continue to improve towards automated driving, new methods are required for processing the data in an efficient and economical manner from the sensors for such complex systems. The detection of dynamic objects is one of the most important aspects required by advanced driver assistance systems and automated driving. In this thesis, an environment model approach for the detection of dynamic objects is presented in order to realize an effective method for sensor data fusion. A scalable high-level fusion architecture is developed for fusing object data from several sensors in a single system, where processing occurs in three levels: sensor, fusion and application. A complete and consistent object model which includes the object’s dynamic state, existence probability and classification is defined as a sensor-independent and generic interface for sensor data fusion across all three processing levels. Novel algorithms are developed for object data association and fusion at the fusion-level of the architecture. An asynchronous sensor-to-global fusion strategy is applied in order to process sensor data immediately within the high-level fusion architecture, giving driver assistance systems the most up-to-date information about the vehicle’s environment. Track-to-track fusion algorithms are uniquely applied for dynamic state fusion, where the information matrix fusion algorithm produces results comparable to a low-level central Kalman filter approach. The existence probability of an object is fused using a novel approach based on the Dempster-Shafer evidence theory, where the individual sensor’s existence estimation performance is considered during the fusion process. A similar novel approach with the Dempster-Shafer evidence theory is also applied to the fusion of an object’s classification. The developed high-level sensor data fusion architecture and its algorithms are evaluated using a prototype vehicle equipped with 12 sensors for surround environment perception. A thorough evaluation of the complete object model is performed on a closed test track using vehicles equipped with hardware for generating an accurate ground truth. Existence and classification performance is evaluated using labeled data sets from real traffic scenarios. The evaluation demonstrates the accuracy and effectiveness of the proposed sensor data fusion approach. The work presented in this thesis has additionally been extensively used in several research projects as the dynamic object detection platform for automated driving applications on highways in real traffic

2019, UMaine News Press Releases

This is a catalog of press releases put out by the University of Maine Division of Marketing and Communications between January 23, 2019 and December 31, 2019

CIMODE 2016: 3º Congresso Internacional de Moda e Design: proceedings

O CIMODE 2016 é o terceiro Congresso Internacional de Moda e Design, a decorrer de 9 a 12 de maio de 2016 na cidade de Buenos Aires, subordinado ao tema : EM--‐TRAMAS. A presente edição é organizada pela Faculdade de Arquitetura, Desenho e Urbanismo da Universidade de Buenos Aires, em conjunto com o Departamento de Engenharia Têxtil da Universidade do Minho e com a ABEPEM – Associação Brasileira de Estudos e Pesquisa em Moda.info:eu-repo/semantics/publishedVersio

The state of coral reef ecosystems of the United States and Pacific Freely Associated States: 2008

In the past decade, increased awareness regarding the declining condition of U.S. coral reefs has prompted various actions by governmental and non-governmental organizations. Presidential Executive Order 13089 created the U.S. Coral Reef Task Force (USCRTF) in 1998 to coordinate federal and state/territorial activities (Clinton, 1998), and the Coral Reef Conservation Act of 2000 provided Congressional funding for activities to conserve these important ecosystems, including mapping, monitoring and assessment projects carried out through the support of NOAA’s CRCP. Numerous collaborations forged among federal agencies and state, local, non-governmental, academic and private partners now support a variety of monitoring activities. This report shares the results of many of these monitoring activities, relying heavily on quantitative, spatially-explicit data that has been collected in the recent past and comparisons with historical data where possible. The success of this effort can be attributed to the dedication of over 270 report contributors who comprised the expert writing teams in the jurisdictions and contributed to the National Level Activities and National Summary chapters. The scope and content of this report are the result of their dedication to this considerable collaborative effort. Ultimately, the goal of this report is to answer the difficult but vital question: what is the condition of U.S. coral reef ecosystems? The report attempts to base a response on the best available science emerging from coral reef ecosystem monitoring programs in 15 jurisdictions across the country. However, few monitoring programs have been in place for longer than a decade, and many have been initiated only within the past two to five years. A few jurisdictions are just beginning to implement monitoring programs and face challenges stemming from a lack of basic habitat maps and other ecosystem data in addition to adequate training, capacity building, and technical support. There is also a general paucity of historical data describing the condition of ecosystem resources before major human impacts occurred, which limits any attempt to present the current conditions within an historical context and contributes to the phenomenon of shifting baselines (Jackson, 1997; Jackson et al., 2001; Pandolfi et al., 2005)