Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision

Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed

Driver lane change intention inference using machine learning methods.

Lane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways. This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ. Finally, discussions and conclusions are made in Part Ⅵ. A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour.PhD in Transpor

An event portfolio in rural development: an ethnographic investigation of a community's use of sport and cultural events

Sport events have been studied predominantly in isolation from other genres, as single events that have economic or social impacts mainly for urban communities. Yet, the apparent economic and social value of recurring small-scale sport and cultural events in rural communities call for strategic and integrated planning in policies for rural development. This requires that instead of assessing the economic or social benefits of a certain event, a series of interrelated events that comprise a host community's event portfolio can be synergized to derive outcomes through a holistic planning approach that places in concert the economic and social planning of different events. From this perspective, this study examines the event portfolio of a rural community. Ethnographic methods were employed and fieldwork was conducted in Fort Stockton, a small community in South-West Texas. Data collection included participant observation, interviews, review of archival materials and social network analysis. The results show that Fort Stockton's event portfolio is an embedded and eclectic assemblage of sport and cultural performances, collective imaginary and thematic preoccupations of the community that are presented as suitable for spectation. The instrumental connectivity of events bolsters the capacity of the portfolio to serve multiple purposes although strategic cross-leverage is not employed. Thematic continuities among events in the portfolio reaffirm and establish the projected meta-messages within and outside the community. A conceptual synergy lies at the core of each event, which dramatizes the ideological conflict between individualism and collectivism, and translates it to community identity, civic esteem, and economic benefit addressing the public discourse in Fort Stockton and mobilizing resources for event implementations. The institutional framework of Fort Stockton constitutes the basis of its capacity to capitalize on its event portfolio. Event organizers operate within an informal event network that frames their cooperative efforts to host events. Therefore, the event portfolio stands as an embedded system in which an integrated approach is taken about economic and social development by creating synergies between sport and cultural events and in turn using them for consolidating the community and fostering tourism. Finally, the theoretical and practical implications that derive from the study are discussed

To Walk the Earth in Safety (FY2022)

After deadly landmines are removed and booby-traps and improvised explosive devices are cleared, wheat fields are now ready for harvesting, children can run to school on a path, families can return to their partially destroyed homes, and elephants are able to migrate through grasslands. Elsewhere, man-portable air defense systems (MANPADS) and small arms and light weapons were secured or destroyed to prevent terrorists from acquiring them and attacking civilians. These are just some of the successes the U.S. Conventional Weapons Destruction Program achieves day after day, year after year, one step at a time. In fiscal year 2022, the United States again answered the call to confront the threats of landmines, unexploded ordnance, and unsecured small arms and light weapons, to make this world a better place. The dedication of all those involved in the removal of these hazards and securing weapons must be commended and applauded every time a life is saved due to their efforts. These successes are well documented in this year’s To Walk the Earth in Safety

PRIMA General Observer Science Book

PRIMA (The PRobe for-Infrared Mission for Astrophysics) is a concept for a far-infrared (IR) observatory. PRIMA features a cryogenically cooled 1.8 m diameter telescope and is designed to carry two science instruments enabling ultra-high sensitivity imaging and spectroscopic studies in the 24 to 235 microns wavelength range. The resulting observatory is a powerful survey and discovery machine, with mapping speeds better by 2 - 4 orders of magnitude with respect to its far-IR predecessors. The bulk of the observing time on PRIMA should be made available to the community through a General Observer (GO) program offering 75% of the mission time over 5 years. In March 2023, the international astronomy community was encouraged to prepare authored contributions articulating scientific cases that are enabled by the telescope massive sensitivity advance and broad spectral coverage, and that could be performed within the context of GO program. This document, the PRIMA General Observer Science Book, is the edited collection of the 76 received contributions.Comment: A. Moullet, T. Kataria, D. Lis, S. Unwin, Y. Hasegawa, E. Mills, C. Battersby, A. Roc, M. Meixner are the editors of the PRIMA General Observer Science Book. The book compiles 76 authored contributions. 399 page

Success factors and performance outcomes of M & As : new perspectives and empirical evidence

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Flood dynamics derived from video remote sensing

Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science