16,102 research outputs found
Quality-Aware Broadcasting Strategies for Position Estimation in VANETs
The dissemination of vehicle position data all over the network is a
fundamental task in Vehicular Ad Hoc Network (VANET) operations, as
applications often need to know the position of other vehicles over a large
area. In such cases, inter-vehicular communications should be exploited to
satisfy application requirements, although congestion control mechanisms are
required to minimize the packet collision probability. In this work, we face
the issue of achieving accurate vehicle position estimation and prediction in a
VANET scenario. State of the art solutions to the problem try to broadcast the
positioning information periodically, so that vehicles can ensure that the
information their neighbors have about them is never older than the
inter-transmission period. However, the rate of decay of the information is not
deterministic in complex urban scenarios: the movements and maneuvers of
vehicles can often be erratic and unpredictable, making old positioning
information inaccurate or downright misleading. To address this problem, we
propose to use the Quality of Information (QoI) as the decision factor for
broadcasting. We implement a threshold-based strategy to distribute position
information whenever the positioning error passes a reference value, thereby
shifting the objective of the network to limiting the actual positioning error
and guaranteeing quality across the VANET. The threshold-based strategy can
reduce the network load by avoiding the transmission of redundant messages, as
well as improving the overall positioning accuracy by more than 20% in
realistic urban scenarios.Comment: 8 pages, 7 figures, 2 tables, accepted for presentation at European
Wireless 201
Coherence in Large-Scale Networks: Dimension-Dependent Limitations of Local Feedback
We consider distributed consensus and vehicular formation control problems.
Specifically we address the question of whether local feedback is sufficient to
maintain coherence in large-scale networks subject to stochastic disturbances.
We define macroscopic performance measures which are global quantities that
capture the notion of coherence; a notion of global order that quantifies how
closely the formation resembles a solid object. We consider how these measures
scale asymptotically with network size in the topologies of regular lattices in
1, 2 and higher dimensions, with vehicular platoons corresponding to the 1
dimensional case. A common phenomenon appears where a higher spatial dimension
implies a more favorable scaling of coherence measures, with a dimensions of 3
being necessary to achieve coherence in consensus and vehicular formations
under certain conditions. In particular, we show that it is impossible to have
large coherent one dimensional vehicular platoons with only local feedback. We
analyze these effects in terms of the underlying energetic modes of motion,
showing that they take the form of large temporal and spatial scales resulting
in an accordion-like motion of formations. A conclusion can be drawn that in
low spatial dimensions, local feedback is unable to regulate large-scale
disturbances, but it can in higher spatial dimensions. This phenomenon is
distinct from, and unrelated to string instability issues which are commonly
encountered in control problems for automated highways.Comment: To appear in IEEE Trans. Automat. Control; 15 pages, 2 figure
A two-dimensional data-driven model for traffic flow on highways
Based on experimental traffic data obtained from German and US highways, we
propose a novel two-dimensional first-order macroscopic traffic flow model. The
goal is to reproduce a detailed description of traffic dynamics for the real
road geometry. In our approach both the dynamic along the road and across the
lanes is continuous. The closure relations, being necessary to complete the
hydrodynamic equation, are obtained by regression on fundamental diagram data.
Comparison with prediction of one-dimensional models shows the improvement in
performance of the novel model.Comment: 27 page
A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks
Situational awareness in vehicular networks could be substantially improved
utilizing reliable trajectory prediction methods. More precise situational
awareness, in turn, results in notably better performance of critical safety
applications, such as Forward Collision Warning (FCW), as well as comfort
applications like Cooperative Adaptive Cruise Control (CACC). Therefore,
vehicle trajectory prediction problem needs to be deeply investigated in order
to come up with an end to end framework with enough precision required by the
safety applications' controllers. This problem has been tackled in the
literature using different methods. However, machine learning, which is a
promising and emerging field with remarkable potential for time series
prediction, has not been explored enough for this purpose. In this paper, a
two-layer neural network-based system is developed which predicts the future
values of vehicle parameters, such as velocity, acceleration, and yaw rate, in
the first layer and then predicts the two-dimensional, i.e. longitudinal and
lateral, trajectory points based on the first layer's outputs. The performance
of the proposed framework has been evaluated in realistic cut-in scenarios from
Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable
improvement in the prediction accuracy in comparison with the kinematics model
which is the dominant employed model by the automotive industry. Both ideal and
nonideal communication circumstances have been investigated for our system
evaluation. For non-ideal case, an estimation step is included in the framework
before the parameter prediction block to handle the drawbacks of packet drops
or sensor failures and reconstruct the time series of vehicle parameters at a
desirable frequency
A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process
Scalability is one of the major issues for real-world Vehicle-to-Vehicle
network realization. To tackle this challenge, a stochastic hybrid modeling
framework based on a non-parametric Bayesian inference method, i.e.,
hierarchical Dirichlet process (HDP), is investigated in this paper. This
framework is able to jointly model driver/vehicle behavior through forecasting
the vehicle dynamical time-series. This modeling framework could be merged with
the notion of model-based information networking, which is recently proposed in
the vehicular literature, to overcome the scalability challenges in dense
vehicular networks via broadcasting the behavioral models instead of raw
information dissemination. This modeling approach has been applied on several
scenarios from the realistic Safety Pilot Model Deployment (SPMD) driving data
set and the results show a higher performance of this model in comparison with
the zero-hold method as the baseline.Comment: This is the accepted version of the paper in 2018 IEEE 88th Vehicular
Technology Conference (VTC2018-Fall) (references added, title and abstract
modified
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