Developing a global risk engine

Risk analysis is a critical link in the reduction of casualties and damages due to earthquakes. Recognition of this relation has led to a rapid rise in demand for accurate, reliable and flexible risk assessment software. However, there is a significant disparity between the high quality scientific data developed by researchers and the availability of versatile, open and user-friendly risk analysis tools to meet the demands of end-users. In the past few years several open-source software have been developed that play an important role in the seismic research, such as OpenSHA and OpenSEES. There is however still a gap when it comes to open-source risk assessment tools and software. In order to fill this gap, the Global Earthquake Model (GEM) has been created. GEM is an internationally sanctioned program initiated by the OECD that aims to build independent, open standards to calculate and communicate earthquake risk around the world. This initiative started with a one-year pilot project named GEM1, during which an evaluation of a number of existing risk software was carried out. After a critical review of the results it was concluded that none of the software were adequate for GEM requirements and therefore, a new object-oriented tool was to be developed. This paper presents a summary of some of the most well known applications used in risk analysis, highlighting the main aspects that were considered for the development of this risk platform. The research that was carried out in order to gather all of the necessary information to build this tool was distributed in four different areas: information technology approach, seismic hazard resources, vulnerability assessment methodologies and sources of exposure data. The main aspects and findings for each of these areas will be presented as well as how these features were incorporated in the up-to-date risk engine. Currently, the risk engine is capable of predicting human or economical losses worldwide considering both deterministic and probabilistic-based events, using vulnerability curves. A first version of GEM will become available at the end of 2013. Until then the risk engine will continue to be developed by a growing community of developers, using a dedicated open-source platform

The aceToolbox: low-level audiovisual feature extraction for retrieval and classification

In this paper we present an overview of a software platform that has been developed within the aceMedia project, termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 eXperimental Model (XM), with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images

Pedestrian Flow Simulation Validation and Verification Techniques

For the verification and validation of microscopic simulation models of pedestrian flow, we have performed experiments for different kind of facilities and sites where most conflicts and congestion happens e.g. corridors, narrow passages, and crosswalks. The validity of the model should compare the experimental conditions and simulation results with video recording carried out in the same condition like in real life e.g. pedestrian flux and density distributions. The strategy in this technique is to achieve a certain amount of accuracy required in the simulation model. This method is good at detecting the critical points in the pedestrians walking areas. For the calibration of suitable models we use the results obtained from analyzing the video recordings in Hajj 2009 and these results can be used to check the design sections of pedestrian facilities and exits. As practical examples, we present the simulation of pilgrim streams on the Jamarat bridge. The objectives of this study are twofold: first, to show through verification and validation that simulation tools can be used to reproduce realistic scenarios, and second, gather data for accurate predictions for designers and decision makers.Comment: 19 pages, 10 figure

CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data

This paper presents a novel method for ground segmentation in Velodyne point clouds. We propose an encoding of sparse 3D data from the Velodyne sensor suitable for training a convolutional neural network (CNN). This general purpose approach is used for segmentation of the sparse point cloud into ground and non-ground points. The LiDAR data are represented as a multi-channel 2D signal where the horizontal axis corresponds to the rotation angle and the vertical axis the indexes channels (i.e. laser beams). Multiple topologies of relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained and evaluated using a manually annotated dataset we prepared. The results show significant improvement of performance over the state-of-the-art method by Zhang et al. in terms of speed and also minor improvements in terms of accuracy.Comment: ICRA 2018 submissio

Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model

Background Road collisions and casualties pose a serious threat to commuters around the globe. Autonomous Vehicles (AVs) aim to make the use of technology to reduce the road accidents. However, the most of research work in the context of collision avoidance has been performed to address, separately, the rear end, front end and lateral collisions in less congested and with high inter-vehicular distances. Purpose The goal of this paper is to introduce the concept of a social agent, which interact with other AVs in social manners like humans are social having the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. The proposed social agent is based on a human-brain inspired mentalizing and mirroring capabilities and has been modelled for collision detection and avoidance under congested urban road traffic. Method We designed our social agent having the capabilities of mentalizing and mirroring and for this purpose we utilized Exploratory Agent Based Modeling (EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by Niazi and Hussain. Results Our simulation and practical experiments reveal that by embedding Richardson's arms race model within AVs, collisions can be avoided while travelling on congested urban roads in a flock like topologies. The performance of the proposed social agent has been compared at two different levels.Comment: 48 pages, 21 figure