3,892 research outputs found
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
Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning
Although aviation accidents are rare, safety incidents occur more frequently
and require a careful analysis to detect and mitigate risks in a timely manner.
Analyzing safety incidents using operational data and producing event-based
explanations is invaluable to airline companies as well as to governing
organizations such as the Federal Aviation Administration (FAA) in the United
States. However, this task is challenging because of the complexity involved in
mining multi-dimensional heterogeneous time series data, the lack of
time-step-wise annotation of events in a flight, and the lack of scalable tools
to perform analysis over a large number of events. In this work, we propose a
precursor mining algorithm that identifies events in the multidimensional time
series that are correlated with the safety incident. Precursors are valuable to
systems health and safety monitoring and in explaining and forecasting safety
incidents. Current methods suffer from poor scalability to high dimensional
time series data and are inefficient in capturing temporal behavior. We propose
an approach by combining multiple-instance learning (MIL) and deep recurrent
neural networks (DRNN) to take advantage of MIL's ability to learn using weakly
supervised data and DRNN's ability to model temporal behavior. We describe the
algorithm, the data, the intuition behind taking a MIL approach, and a
comparative analysis of the proposed algorithm with baseline models. We also
discuss the application to a real-world aviation safety problem using data from
a commercial airline company and discuss the model's abilities and
shortcomings, with some final remarks about possible deployment directions
Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R
This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud
To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud
A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
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