Abstract:Many applications, such as autonomous navigation, urban planning, and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper, we propose the automatic detection and computation of the coordinates of recurring stationary objects of interest using street view imagery. Our processing pipeline relies on two fully convolutional neural networks: the first segments objects in the images, while the second estimates their distance from the camera. To geolocate all the detected objects coherently we propose a novel custom Markov random field model to estimate the objects' geolocation. The novelty of the resulting pipeline is the combined use of monocular depth estimation and triangulation to enable automatic mapping of complex scenes with the simultaneous presence of multiple, visually similar objects of interest. We validate experimentally the effectiveness of our approach on two object classes: traffic lights and telegraph poles. The experiments report high object recall rates and position precision of approximately 2 m, which is approaching the precision of single-frequency GPS receivers.

The increase in mobile communications traffic has led to heightened interest in the use of deterministic propagation methods together with digital building and terrain databases for propagation prediction in urban areas. Ray methods have been particularly popular, and there are many papers in the literature describing the performance of various approaches to ray tracing. This paper will describe a powerful, recently developed, threedimensional (3-D) software suite for urban propagation modeling which, although based on 3-D ray tracing (using images), draws upon an ensemble of propagation tools including physical optics using the fast far-field approximation, the parabolic equation approximation for propagation over multiple buildings, and uniform theory of diffraction (UTD). Many ray-tracing methods determine ray paths between the transmitter and a single arbitrary receiver. This paper adopts an approach of a transmitter to a multiple receiver technique, resulting in greatly reduced computational times. The associated method can handle both indoor and outdoor propagation on both flat and undulating terrain. Terrain is represented as a set of triangular two-dimensional (2-D) patches, while buildings and clutter are represented using layers of polygons.

Mobility is associated with driving a vehicle. Age-related declines in the abilities of older persons present certain obstacles to safe driving. The negative effects of driving cessation on older adults' physical, mental, cognitive, and social functioning are well reported. Automated driving solutions represent a potential solution to promoting driver persistence and the management of fitness to drive issues in older adults. Technology innovation influences societal values and raises ethical questions. The advancement of new driving solutions raises overarching questions in relation to the values of society and how we design technology (a) to promote positive values around ageing, (b) to enhance ageing experience, (c) to protect human rights, (d) to ensure human benefit and (e) to prioritise human well-being. To this end, this chapter reviews the relevant ethical considerations in relation to assisted driving solutions. Further, it presents a new ethically aligned system concept for assisted driving. It is argued that human benefit, well-being and respect for human identity and rights are important goals for new automated driving technologies. Enabling driver persistence is an issue for all of society and not just older adult.

This paper describes a point to multipoint three-dimensional convex space-based ray-tracing technique. This visibility list is calculated and stored and can be reused as needed. What distinguishes our method is that the visibility list is transmitter location independent, is a three dimensional implementation and is highly computationally efficient. The division of the building into free and filled convex spaces leads to an efficient Method of Images reflection and diffraction path generation algorithm. This technique can be used to optimise the locations of base transceivers in a highly efficient manner. The first step in producing this tool is the generation of efficient ray-tracing algorithms. The ray-tracing algorithm was specifically designed for later incorporation into a transmitter optimisation algorithm. This requires a fast ray-tracing method because of its computationally intensive needs -running multiple times over a point-to-multipoint grid. Our algorithm is executed for sample building environments and then for a real building and compared with measurements to confirm its validity. It is clear that the results are in good agreement but do indicate that a highly accurate spatial modeling of the building is required.

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