3 research outputs found
Metis: Multi-Agent Based Crisis Simulation System
With the advent of the computational technologies (Graphics Processing Units
- GPUs) and Machine Learning, the research domain of crowd simulation for
crisis management has flourished. Along with the new techniques and
methodologies that have been proposed all those years, aiming to increase the
realism of crowd simulation, several crisis simulation systems/tools have been
developed, but most of them focus on special cases without providing users the
ability to adapt them based on their needs. Towards these directions, in this
paper, we introduce a novel multi-agent-based crisis simulation system for
indoor cases. The main advantage of the system is its ease of use feature,
focusing on non-expert users (users with little to no programming skills) that
can exploit its capabilities a, adapt the entire environment based on their
needs (Case studies) and set up building evacuation planning experiments with
some of the most popular Reinforcement Learning algorithms. Simply put, the
system's features focus on dynamic environment design and crisis management,
interconnection with popular Reinforcement Learning libraries, agents with
different characteristics (behaviors), fire propagation parameterization,
realistic physics based on popular game engine, GPU-accelerated agents training
and simulation end conditions. A case study exploiting a popular reinforcement
learning algorithm, for training of the agents, presents the dynamics and the
capabilities of the proposed systems and the paper is concluded with the
highlights of the system and some future directions
Sub-Goal Social Force Model for Collective Pedestrian Motion Under Vehicle Influence
In mixed traffic scenarios, a certain number of pedestrians might coexist in
a small area while interacting with vehicles. In this situation, every
pedestrian must simultaneously react to the surrounding pedestrians and
vehicles. Analytical modeling of such collective pedestrian motion can benefit
intelligent transportation practices like shared space design and urban
autonomous driving. This work proposed the sub-goal social force model (SG-SFM)
to describe the collective pedestrian motion under vehicle influence. The
proposed model introduced a new design of vehicle influence on pedestrian
motion, which was smoothly combined with the influence of surrounding
pedestrians using the sub-goal concept. This model aims to describe generalized
pedestrian motion, i.e., it is applicable to various vehicle-pedestrian
interaction patterns. The generalization was verified by both quantitative and
qualitative evaluation. The quantitative evaluation was conducted to reproduce
pedestrian motion in three different datasets, HBS, CITR, and DUT. It also
compared two different ways of calibrating the model parameters. The
qualitative evaluation examined the simulation of collective pedestrian motion
in a series of fundamental vehicle-pedestrian interaction scenarios. The above
evaluation results demonstrated the effectiveness of the proposed model.Comment: submitted to IEEE Transactions on Intelligent Transportation System
Crowd simulation for crisis management: the outcomes of the last decade
The last few decades, crowd simulation for crisis management is highlighted
as an important topic of interest for many scientific fields. As the continues
evolution of computational resources increases, along with the capabilities of
Artificial Intelligence, the demand for better and more realistic simulation
has become more attractive and popular to scientists. Along those years, there
have been published hundreds of research articles and have been created
numerous different systems that aim to simulate crowd behaviors, crisis cases
and emergency evacuation scenarios. For better outcomes, recent research has
focused on the separation of the problem of crisis management, to multiple
research sub-fields (categories), such as the navigation of the simulated
pedestrians, their psychology, the group dynamics etc. There have been extended
research works suggesting new methods and techniques for those categories of
problems. In this paper, we propose three main research categories, each one
consist of several sub-categories, relying on crowd simulation for crisis
management aspects and we present the outcomes of the last decade, focusing
mostly on works exploiting multi-agent technologies. We analyze a number of
technologies, methodologies, techniques, tools and systems introduced
throughout the last years. A comparative review and discussion of the proposed
categories is presented towards the identification of the most efficient
aspects of the proposed categories. A general framework, towards the future
crowd simulation for crisis management is presented based on the most efficient
to yield the most realistic outcomes of the last decades. The paper is
concluded with some highlights and open questions for future directions.Comment: Submitted to Expert Systems with Application