自律的ネットワーク運用のための管理知識の利用手法に関する研究

Tohoku University木下哲男課

Modeling, Evaluation, and Scale on Artificial Pedestrians: A Literature Review

Modeling pedestrian dynamics and their implementation in a computer are challenging and important issues in the knowledge areas of transportation and computer simulation. The aim of this article is to provide a bibliographic outlook so that the reader may have quick access to the most relevant works related to this problem. We have used three main axes to organize the article's contents: pedestrian models, validation techniques, and multiscale approaches. The backbone of this work is the classification of existing pedestrian models; we have organized the works in the literature under five categories, according to the techniques used for implementing the operational level in each pedestrian model. Then the main existing validation methods, oriented to evaluate the behavioral quality of the simulation systems, are reviewed. Furthermore, we review the key issues that arise when facing multiscale pedestrian modeling, where we first focus on the behavioral scale (combinations of micro and macro pedestrian models) and second on the scale size (from individuals to crowds). The article begins by introducing the main characteristics of walking dynamics and its analysis tools and concludes with a discussion about the contributions that different knowledge fields can make in the near future to this exciting area

Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity

International audienceWe present a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, we used a branch of complex systems modeling called artificial self-organization in which large sets of software entities interacting locally give rise to bottom-up collective behaviors. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model recovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with artificial self-organization. Although there is no evidence yet of the model's connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Interactive Tracking, Prediction, and Behavior Learning of Pedestrians in Dense Crowds

The ability to automatically recognize human motions and behaviors is a key skill for autonomous machines to exhibit to interact intelligently with a human-inhabited environment. The capabilities autonomous machines should have include computing the motion trajectory of each pedestrian in a crowd, predicting his or her position in the near future, and analyzing the personality characteristics of the pedestrian. Such techniques are frequently used for collision-free robot navigation, data-driven crowd simulation, and crowd surveillance applications. However, prior methods for these problems have been restricted to low-density or sparse crowds where the pedestrian movement is modeled using simple motion models. In this thesis, we present several interactive algorithms to extract pedestrian trajectories from videos in dense crowds. Our approach combines different pedestrian motion models with particle tracking and mixture models and can obtain an average of $20\%$ improvement in accuracy in medium-density crowds over prior work. We compute the pedestrian dynamics from these trajectories using Bayesian learning techniques and combine them with global methods for long-term pedestrian prediction in densely crowded settings. Finally, we combine these techniques with Personality Trait Theory to automatically classify the dynamic behavior or the personality of a pedestrian based on his or her movements in a crowded scene. The resulting algorithms are robust and can handle sparse and noisy motion trajectories. We demonstrate the benefits of our long-term prediction and behavior classification methods in dense crowds and highlight the benefits over prior techniques. We highlight the performance of our novel algorithms on three different applications. The first application is interactive data-driven crowd simulation, which includes crowd replication as well as the combination of pedestrian behaviors from different videos. Secondly, we combine the prediction scheme with proxemic characteristics from psychology and use them to perform socially-aware navigation. Finally, we present novel techniques for anomaly detection in low-to medium-density crowd videos using trajectory-level behavior learning.Doctor of Philosoph

A Research Approach to Study Human Factors in Transportation Systems

This thesis proposes a new general-purpose methodology to conduct studies on Human Factors in Transportation Systems.A full-fledged setup and implementation of the methodology is provided for validation. This setup, which uses real data to perform the simulation, includes a traffic micro-simulator, a driving simulator, a traffic control centre and an Advanced Driver Assistance System, providing an experimentation laboratory, in which empirical research can be conducted. The communication between the simulation components is made interchangeably using both the European standard Datex II and the SUMO TraCI protocols.Several usage scenarios are implemented and indications on how to extend the methodology to accommodate different requirements are provided; as to prove its usability and feasibility. A simple Human Factors study was conducted using the implemented setup. This study uses naturalistc data and evaluates the network performance gain by using an Advanced Driver Assistance System that recommends new routes to drivers in congestion situations and provides a final validation of the methodology.In conclusion, the methodology has been proved usable to effectively conduct Human Factors research and also to develop Advanced Driver Assistance Systems applications in a controlled, yet realistic environment.This thesis proposes a new general-purpose methodology to conduct studies on Human Factors in Transportation Systems.A full-fledged setup and implementation of the methodology is provided for validation. This setup, which uses real data to perform the simulation, includes a traffic micro-simulator, a driving simulator, a traffic control centre and an Advanced Driver Assistance System, providing an experimentation laboratory, in which empirical research can be conducted. The communication between the simulation components is made interchangeably using both the European standard Datex II and the SUMO TraCI protocols.Several usage scenarios are implemented and indications on how to extend the methodology to accommodate different requirements are provided; as to prove its usability and feasibility. A simple Human Factors study was conducted using the implemented setup. This study uses naturalistc data and evaluates the network performance gain by using an Advanced Driver Assistance System that recommends new routes to drivers in congestion situations and provides a final validation of the methodology.In conclusion, the methodology has been proved usable to effectively conduct Human Factors research and also to develop Advanced Driver Assistance Systems applications in a controlled, yet realistic environment