25,322 research outputs found
Risk Assessment Algorithms Based On Recursive Neural Networks
The assessment of highly-risky situations at road intersections have been
recently revealed as an important research topic within the context of the
automotive industry. In this paper we shall introduce a novel approach to
compute risk functions by using a combination of a highly non-linear processing
model in conjunction with a powerful information encoding procedure.
Specifically, the elements of information either static or dynamic that appear
in a road intersection scene are encoded by using directed positional acyclic
labeled graphs. The risk assessment problem is then reformulated in terms of an
inductive learning task carried out by a recursive neural network. Recursive
neural networks are connectionist models capable of solving supervised and
non-supervised learning problems represented by directed ordered acyclic
graphs. The potential of this novel approach is demonstrated through well
predefined scenarios. The major difference of our approach compared to others
is expressed by the fact of learning the structure of the risk. Furthermore,
the combination of a rich information encoding procedure with a generalized
model of dynamical recurrent networks permit us, as we shall demonstrate, a
sophisticated processing of information that we believe as being a first step
for building future advanced intersection safety system
Congestion management in traffic-light intersections via Infinitesimal Perturbation Analysis
We present a flow-control technique in traffic-light intersections, aiming at
regulating queue lengths to given reference setpoints. The technique is based
on multivariable integrators with adaptive gains, computed at each control
cycle by assessing the IPA gradients of the plant functions. Moreover, the IPA
gradients are computable on-line despite the absence of detailed models of the
traffic flows. The technique is applied to a two-intersection system where it
exhibits robustness with respect to modeling uncertainties and computing
errors, thereby permitting us to simplify the on-line computations perhaps at
the expense of accuracy while achieving the desired tracking. We compare, by
simulation, the performance of a centralized, joint two-intersection control
with distributed control of each intersection separately, and show similar
performance of the two control schemes for a range of parameters
A Case Study on Formal Verification of Self-Adaptive Behaviors in a Decentralized System
Self-adaptation is a promising approach to manage the complexity of modern
software systems. A self-adaptive system is able to adapt autonomously to
internal dynamics and changing conditions in the environment to achieve
particular quality goals. Our particular interest is in decentralized
self-adaptive systems, in which central control of adaptation is not an option.
One important challenge in self-adaptive systems, in particular those with
decentralized control of adaptation, is to provide guarantees about the
intended runtime qualities. In this paper, we present a case study in which we
use model checking to verify behavioral properties of a decentralized
self-adaptive system. Concretely, we contribute with a formalized architecture
model of a decentralized traffic monitoring system and prove a number of
self-adaptation properties for flexibility and robustness. To model the main
processes in the system we use timed automata, and for the specification of the
required properties we use timed computation tree logic. We use the Uppaal tool
to specify the system and verify the flexibility and robustness properties.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432
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