1,174 research outputs found
Spectral Clustering and Integration: The Inner Dynamics of Computational Geometry and Spatial Morphology
Deviating from common evaluation strategies of spatial networks that are realised through numerical comparison of single floating-point numbers such as global and local space syntax measures (centralities, connectivity, etc.) we aim to present a new computational methodology for creating detailed topo-geometric encodings of spaces that encapsulate some of the fundamental ideas about spatial morphology by Hillier (Space is the Machine: A Configurational Theory of Architecture, London, UK, Space Syntax, 2007 [1]). In most cases, space syntax measures try to capture a particular quality of the space for comparison but they lose much of the detail of the spatial topo-geometry and morphology by mainly aggregating graph path traversals and not retaining any other information. This research explores the use of weighted graph spectra, in a composite form, for the purpose of characterising the spatial structure as a whole. The new methodology focuses on the three primary space syntax graph modelling concepts, ‘angular’, ‘metric’ and ‘topological’, from the point of view of the resulting spatial geometries and develops new computational innovations in order to map spatial penetration of local neighbourhood spectra in different scales, dimensions and built environment densities in a continues way. The result is a new composite vector of high dimensionality that can be easily measured against others for detailed comparison. The proposed methodology is then demonstrated with the complete road-network dataset of Great Britain. The main dataset together with subsets is then used in a series of unsupervised machine learning analyses, including clustering and a form of Euclidian ‘spectral integration’
VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene
High-definition (HD) map serves as the essential infrastructure of autonomous
driving. In this work, we build up a systematic vectorized map annotation
framework (termed VMA) for efficiently generating HD map of large-scale driving
scene. We design a divide-and-conquer annotation scheme to solve the spatial
extensibility problem of HD map generation, and abstract map elements with a
variety of geometric patterns as unified point sequence representation, which
can be extended to most map elements in the driving scene. VMA is highly
efficient and extensible, requiring negligible human effort, and flexible in
terms of spatial scale and element type. We quantitatively and qualitatively
validate the annotation performance on real-world urban and highway scenes, as
well as NYC Planimetric Database. VMA can significantly improve map generation
efficiency and require little human effort. On average VMA takes 160min for
annotating a scene with a range of hundreds of meters, and reduces 52.3% of the
human cost, showing great application value
The path inference filter: model-based low-latency map matching of probe vehicle data
We consider the problem of reconstructing vehicle trajectories from sparse
sequences of GPS points, for which the sampling interval is between 10 seconds
and 2 minutes. We introduce a new class of algorithms, called altogether path
inference filter (PIF), that maps GPS data in real time, for a variety of
trade-offs and scenarios, and with a high throughput. Numerous prior approaches
in map-matching can be shown to be special cases of the path inference filter
presented in this article. We present an efficient procedure for automatically
training the filter on new data, with or without ground truth observations. The
framework is evaluated on a large San Francisco taxi dataset and is shown to
improve upon the current state of the art. This filter also provides insights
about driving patterns of drivers. The path inference filter has been deployed
at an industrial scale inside the Mobile Millennium traffic information system,
and is used to map fleets of data in San Francisco, Sacramento, Stockholm and
Porto.Comment: Preprint, 23 pages and 23 figure
AutoGraph: Predicting Lane Graphs from Traffic Observations
Lane graph estimation is a long-standing problem in the context of autonomous
driving. Previous works aimed at solving this problem by relying on
large-scale, hand-annotated lane graphs, introducing a data bottleneck for
training models to solve this task. To overcome this limitation, we propose to
use the motion patterns of traffic participants as lane graph annotations. In
our AutoGraph approach, we employ a pre-trained object tracker to collect the
tracklets of traffic participants such as vehicles and trucks. Based on the
location of these tracklets, we predict the successor lane graph from an
initial position using overhead RGB images only, not requiring any human
supervision. In a subsequent stage, we show how the individual successor
predictions can be aggregated into a consistent lane graph. We demonstrate the
efficacy of our approach on the UrbanLaneGraph dataset and perform extensive
quantitative and qualitative evaluations, indicating that AutoGraph is on par
with models trained on hand-annotated graph data. Model and dataset will be
made available at redacted-for-review.Comment: 8 pages, 6 figure
Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images
Self-driving vehicles rely on urban street maps for autonomous navigation. In
this paper, we introduce Pix2Map, a method for inferring urban street map
topology directly from ego-view images, as needed to continually update and
expand existing maps. This is a challenging task, as we need to infer a complex
urban road topology directly from raw image data. The main insight of this
paper is that this problem can be posed as cross-modal retrieval by learning a
joint, cross-modal embedding space for images and existing maps, represented as
discrete graphs that encode the topological layout of the visual surroundings.
We conduct our experimental evaluation using the Argoverse dataset and show
that it is indeed possible to accurately retrieve street maps corresponding to
both seen and unseen roads solely from image data. Moreover, we show that our
retrieved maps can be used to update or expand existing maps and even show
proof-of-concept results for visual localization and image retrieval from
spatial graphs.Comment: 12 pages, 8 figure
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