2,807 research outputs found

    Game Theory For Self-Driving Cars

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    Pedestrian behaviour understanding is of utmost importance for autonomous vehicles (AVs). Pedestrian behaviour is complex and harder to model and predict than other road users such as drivers and cyclists. In this paper, we present an overview of our ongoing work on modelling AV-human interactions using game theory for autonomous vehicles control

    Empiricism and stochastics in cellular automaton modeling of urban land use dynamics

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    An increasing number of models for predicting land use change in regions of rapidurbanization are being proposed and built using ideas from cellular automata (CA)theory. Calibrating such models to real situations is highly problematic and to date,serious attention has not been focused on the estimation problem. In this paper, wepropose a structure for simulating urban change based on estimating land usetransitions using elementary probabilistic methods which draw their inspiration fromBayes' theory and the related ?weights of evidence? approach. These land use changeprobabilities drive a CA model ? DINAMICA ? conceived at the Center for RemoteSensing of the Federal University of Minas Gerais (CSR-UFMG). This is based on aneight cell Moore neighborhood approach implemented through empirical land useallocation algorithms. The model framework has been applied to a medium-size townin the west of São Paulo State, Bauru. We show how various socio-economic andinfrastructural factors can be combined using the weights of evidence approach whichenables us to predict the probability of changes between land use types in differentcells of the system. Different predictions for the town during the period 1979-1988were generated, and statistical validation was then conducted using a multipleresolution fitting procedure. These modeling experiments support the essential logicof adopting Bayesian empirical methods which synthesize various information aboutspatial infrastructure as the driver of urban land use change. This indicates therelevance of the approach for generating forecasts of growth for Brazilian citiesparticularly and for world-wide cities in general

    Interplay between interferences and electron-electron interactions in epitaxial graphene

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    We separate localization and interaction effects in epitaxial graphene devices grown on the C-face of a 4H-SiC substrate by analyzing the low temperature conductivities. Weak localization and antilocalization are extracted at low magnetic fields, after elimination of a geometric magnetoresistance and subtraction of the magnetic field dependent Drude conductivity. The electron electron interaction correction is extracted at higher magnetic fields, where localization effects disappear. Both phenomena are weak but sizable and of the same order of magnitude. If compared to graphene on silicon dioxide, electron electron interaction on epitaxial graphene are not significantly reduced by the larger dielectric constant of the SiC substrate
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