7,578 research outputs found
CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a Multilayer Transportation Network
Mobile phone data have recently become an attractive source of information
about mobility behavior. Since cell phone data can be captured in a passive way
for a large user population, they can be harnessed to collect well-sampled
mobility information. In this paper, we propose CT-Mapper, an unsupervised
algorithm that enables the mapping of mobile phone traces over a multimodal
transport network. One of the main strengths of CT-Mapper is its capability to
map noisy sparse cellular multimodal trajectories over a multilayer
transportation network where the layers have different physical properties and
not only to map trajectories associated with a single layer. Such a network is
modeled by a large multilayer graph in which the nodes correspond to
metro/train stations or road intersections and edges correspond to connections
between them. The mapping problem is modeled by an unsupervised HMM where the
observations correspond to sparse user mobile trajectories and the hidden
states to the multilayer graph nodes. The HMM is unsupervised as the transition
and emission probabilities are inferred using respectively the physical
transportation properties and the information on the spatial coverage of
antenna base stations. To evaluate CT-Mapper we collected cellular traces with
their corresponding GPS trajectories for a group of volunteer users in Paris
and vicinity (France). We show that CT-Mapper is able to accurately retrieve
the real cell phone user paths despite the sparsity of the observed trace
trajectories. Furthermore our transition probability model is up to 20% more
accurate than other naive models.Comment: Under revision in Computer Communication Journa
Satellite Navigation for the Age of Autonomy
Global Navigation Satellite Systems (GNSS) brought navigation to the masses.
Coupled with smartphones, the blue dot in the palm of our hands has forever
changed the way we interact with the world. Looking forward, cyber-physical
systems such as self-driving cars and aerial mobility are pushing the limits of
what localization technologies including GNSS can provide. This autonomous
revolution requires a solution that supports safety-critical operation,
centimeter positioning, and cyber-security for millions of users. To meet these
demands, we propose a navigation service from Low Earth Orbiting (LEO)
satellites which deliver precision in-part through faster motion, higher power
signals for added robustness to interference, constellation autonomous
integrity monitoring for integrity, and encryption / authentication for
resistance to spoofing attacks. This paradigm is enabled by the 'New Space'
movement, where highly capable satellites and components are now built on
assembly lines and launch costs have decreased by more than tenfold. Such a
ubiquitous positioning service enables a consistent and secure standard where
trustworthy information can be validated and shared, extending the electronic
horizon from sensor line of sight to an entire city. This enables the
situational awareness needed for true safe operation to support autonomy at
scale.Comment: 11 pages, 8 figures, 2020 IEEE/ION Position, Location and Navigation
Symposium (PLANS
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
Predicting the future location of vehicles is essential for safety-critical
applications such as advanced driver assistance systems (ADAS) and autonomous
driving. This paper introduces a novel approach to simultaneously predict both
the location and scale of target vehicles in the first-person (egocentric) view
of an ego-vehicle. We present a multi-stream recurrent neural network (RNN)
encoder-decoder model that separately captures both object location and scale
and pixel-level observations for future vehicle localization. We show that
incorporating dense optical flow improves prediction results significantly
since it captures information about motion as well as appearance change. We
also find that explicitly modeling future motion of the ego-vehicle improves
the prediction accuracy, which could be especially beneficial in intelligent
and automated vehicles that have motion planning capability. To evaluate the
performance of our approach, we present a new dataset of first-person videos
collected from a variety of scenarios at road intersections, which are
particularly challenging moments for prediction because vehicle trajectories
are diverse and dynamic.Comment: To appear on ICRA 201
Vision-based localization methods under GPS-denied conditions
This paper reviews vision-based localization methods in GPS-denied
environments and classifies the mainstream methods into Relative Vision
Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss
the broad application of optical flow in feature extraction-based Visual
Odometry (VO) solutions and introduce advanced optical flow estimation methods.
For AVL, we review recent advances in Visual Simultaneous Localization and
Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman
Filter (EKF) based methods. We also introduce the application of offline map
registration and lane vision detection schemes to achieve Absolute Visual
Localization. This paper compares the performance and applications of
mainstream methods for visual localization and provides suggestions for future
studies.Comment: 32 pages, 15 figure
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