10,950 research outputs found
Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions
To plan safe trajectories in urban environments, autonomous vehicles must be
able to quickly assess the future intentions of dynamic agents. Pedestrians are
particularly challenging to model, as their motion patterns are often uncertain
and/or unknown a priori. This paper presents a novel changepoint detection and
clustering algorithm that, when coupled with offline unsupervised learning of a
Gaussian process mixture model (DPGP), enables quick detection of changes in
intent and online learning of motion patterns not seen in prior training data.
The resulting long-term movement predictions demonstrate improved accuracy
relative to offline learning alone, in terms of both intent and trajectory
prediction. By embedding these predictions within a chance-constrained motion
planner, trajectories which are probabilistically safe to pedestrian motions
can be identified in real-time. Hardware experiments demonstrate that this
approach can accurately predict pedestrian motion patterns from onboard
sensor/perception data and facilitate robust navigation within a dynamic
environment.Comment: Submitted to 2014 International Workshop on the Algorithmic
Foundations of Robotic
Risk-Aware Management of Distributed Energy Resources
High wind energy penetration critically challenges the economic dispatch of
current and future power systems. Supply and demand must be balanced at every
bus of the grid, while respecting transmission line ratings and accounting for
the stochastic nature of renewable energy sources. Aligned to that goal, a
network-constrained economic dispatch is developed in this paper. To account
for the uncertainty of renewable energy forecasts, wind farm schedules are
determined so that they can be delivered over the transmission network with a
prescribed probability. Given that the distribution of wind power forecasts is
rarely known, and/or uncertainties may yield non-convex feasible sets for the
power schedules, a scenario approximation technique using Monte Carlo sampling
is pursued. Upon utilizing the structure of the DC optimal power flow (OPF), a
distribution-free convex problem formulation is derived whose complexity scales
well with the wind forecast sample size. The efficacy of this novel approach is
evaluated over the IEEE 30-bus power grid benchmark after including real
operation data from seven wind farms.Comment: To appear in Proc. of 18th Intl. Conf. on DSP, Santorini Island,
Greece, July 1-3, 201
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Evaluating the resilience and security of boundaryless, evolving socio-technical Systems of Systems
Efficient Time and Space Representation of Uncertain Event Data
Process mining is a discipline which concerns the analysis of execution data
of operational processes, the extraction of models from event data, the
measurement of the conformance between event data and normative models, and the
enhancement of all aspects of processes. Most approaches assume that event data
is accurately capture behavior. However, this is not realistic in many
applications: data can contain uncertainty, generated from errors in recording,
imprecise measurements, and other factors. Recently, new methods have been
developed to analyze event data containing uncertainty; these techniques
prominently rely on representing uncertain event data by means of graph-based
models explicitly capturing uncertainty. In this paper, we introduce a new
approach to efficiently calculate a graph representation of the behavior
contained in an uncertain process trace. We present our novel algorithm, prove
its asymptotic time complexity, and show experimental results that highlight
order-of-magnitude performance improvements for the behavior graph
construction.Comment: 34 pages, 16 figures, 5 table
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