Behavior-Based Simulation of Real-Time Crowd Evacuation

2011 ◽  
Author(s):  
Changbo Wang ◽  
Chenhui Li ◽  
Yuhua Liu ◽  
Jin Cui ◽  
Tianlun Zhang
Keyword(s):  
Real Time ◽  
Crowd Evacuation ◽  
Behavior Based

Industry First AI-Powered Fully Automated Safety Observation System Deployed to Global Offshore Fleet

2021 ◽  
Author(s):  
Nassima Brown ◽  
Adrian Brown ◽  
Abhijeet Degupta ◽  
Barry Quinn ◽  
Dustin Stringer ◽  
...  
Keyword(s):  
Real Time ◽  
Safety Culture ◽  
Safety Management ◽  
Digital Technologies ◽  
Observation System ◽  
Safety Program ◽  
Effective Application ◽  
Behavior Based ◽  
Global Operations ◽  
The Impact

Abstract As the oil and gas industry is facing tumultuous challenges, adoption of cutting-edge digital technologies has been accelerated to deliver safer, more efficient operations with less impact on the environment. While advanced AI and other digital technologies have been rapidly evolving in many fields in the industry, the HSE sector is playing catch-up. With the increasing complexity of risks and safety management processes, the effective application of data-driven technologies has become significantly harder, particularly for international organizations with varying levels of digital readiness across diverse global operations. Leaders are more cautious to implement solutions that are not fit-for purpose, due to concerns over inconsistencies in rolling out the program across international markets and the impact this may have on ongoing operations. This paper describes how the effective application of Artificial intelligence (AI) and Machine Learning (ML) technologies have been used to engineer a solution that fully digitizes and automates the end-to-end offshore behavior-based safety program across a global offshore fleet; optimizing a critical safety process used by many leading oil & gas organization to drive positive workplace safety culture. The complex safety program has been transformed into clear, efficient and automated workflow, with real-time analytics and live transparent dashboards which detail critical safety indicators in real time, aiding decision-making and improving operational performance. The novel behavior-based safety digital solution, referred to as 3C observation tool within Noble drilling, has been built to be fully aligned with the organization's safety management system requirements and procedures, using modern and agile tools and applications for fully scalability and easy deployment. It has been critical in sharpening the offshore safety observation program across global operations, resulting in a boost of the workforce engagement by 30%, and subsequently increasing safety awareness skill set attainment; improving overall offshore safety culture, all while reducing operating costs by up to 70% and cutting carbon footprint through the elimination of 15,000 manhours and half a million paper cards each year, when compared to previously used methods and workflows

scholarly journals An Evacuation Route Model of Crowd Based on Emotion and Geodesic

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Bangquan Liu ◽  
Zhen Liu ◽  
Dechao Sun ◽  
Chunyue Bi
Keyword(s):  
Real Time ◽  
Geometric Constraints ◽  
Crowd Density ◽  
Time Simulation ◽  
Critical Issues ◽  
Particle Hydrodynamics ◽  
Crowd Evacuation ◽  
Dense Crowd ◽  
Evacuation Route ◽  
Real Time Simulations

Making unconventional emergent plan for dense crowd is one of the critical issues of evacuation simulations. In order to make the behavior of crowd more believable, we present a real-time evacuation route approach based on emotion and geodesic under the influence of individual emotion and multi-hazard circumstances. The proposed emotion model can reflect the dynamic process of individual in group on three factors: individual emotion, perilous field, and crowd emotion. Specifically, we first convert the evacuation scene to Delaunay triangulation representations. Then, we use the optimization-driven geodesic approach to calculate the best evacuation path with user-specified geometric constraints, such as crowd density, obstacle information, and perilous field. Finally, the Smooth Particle Hydrodynamics method is used for local avoidance of collisions with nearby agents in real-time simulation. Extensive experimental results show that our algorithm is efficient and well suited for real-time simulations of crowd evacuation.

IoT-based real time intelligent routing for emergent crowd evacuation

2019 ◽  
Vol 37 (3) ◽  
pp. 604-624
Author(s):  
Yanlan Mei ◽  
Ping Gui ◽  
Xianfeng Luo ◽  
Benbu Liang ◽  
Liuliu Fu ◽  
...  
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Programming Model ◽  
Emergency Evacuation ◽  
Guidance System ◽  
Programming Approach ◽  
Metro Station ◽  
Content Type ◽  
Crowd Evacuation ◽  
Evacuation Route

Purpose The purpose of this paper is to take advantage of Internet of Things (IoT) for intelligent route programming of crowd emergency evacuation in metro station. It is a novel approach to ensure the crowd safety and reduce the casualties in the emergency context. An evacuation route programming model is constructed to select a suitable evacuation route and support the emergency decision maker of metro station. Design/methodology/approach The IoT technology is employed to collect and screen information, and to construct an expert decision model to support the metro station manager to make decision. As a feasible way to solve the multiple criteria decision-making problem, an improved multi-attributive border approximation area comparison (MABAC) approach is introduced. Findings The case study indicates that the model provides valuable suggestions for evacuation route programming and offers practical support for the design of an evacuation route guidance system. Moreover, IoT plays an important role in the process of intelligent route programming of crowd emergency evacuation in metro station. A library has similar structure and crowd characteristics of a metro station, thus the intelligent route programming approach can be applied to the library crowd evacuation. Originality/value The highlights of this paper are listed as followings: the accuracy and accessibility of the metro station’s real-time information are improved by integrating IoT technology with the intelligent route programming of crowd emergency evacuation. An improved MABAC approach is introduced to the expert support model. It promotes the applicability and reliability of decision making for emergency evacuation route selection in metro station. It is a novel way to combine the decision-making methods with practice.

scholarly journals Using Sun’s Java Real-Time System to Manage Behavior-Based Mobile Robot Controllers

2011 ◽  
Vol 2011 ◽  
pp. 1-11
Author(s):  
Andrew McKenzie ◽  
Shameka Dawson ◽  
Fei Hu ◽  
Monica Anderson
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Challenging Behavior ◽  
Timing Constraints ◽  
Time System ◽  
Real Time System ◽  
Garbage Collector ◽  
The Real ◽  
Robot Controller ◽  
Behavior Based

Implementing a robot controller that can effectively manage limited resources in a deterministic, real-time manner is challenging. Behavior-based architectures that decompose autonomy into levels of intelligence are popular due to their robustness but do not provide real-time features that enforce timing constraints or support determinism. We propose an architecture and approach for using the real-time features of the Real-Time Specification for Java (RTSJ) in a behavior-based mobile robot controller to show that timing constraints affect performance. This is accomplished by extending a real-time aware architecture that explicitly enumerates timing requirements for each behavior. It is not enough to reduce latency. The usefulness of this approach is demonstrated via an implementation on Solaris 10 and the Sun Java Real-Time System (Java RTS). Experimental results are obtained using a K-team Koala robot performing path following with four composite behaviors. Experiments were conducted using several task period sets in three cases: real-time threads with the real-time garbage collector, real-time threads with the non- real-time garbage collector, and non-real-time threads with the non-real-time garbage collector. Results show that even if latency and determinism are improved, the timing of each individual behavior significantly affects task performance.

Augmented Affective-Cognition for Usability Study of In-Vehicle System User Interface

2014 ◽  
Vol 14 (2) ◽  
Author(s):  
Feng Zhou ◽  
Yangjian Ji ◽  
Roger J. Jiao
Keyword(s):  
User Interface ◽  
Real Time ◽  
Coherent System ◽  
Emotional States ◽  
Usability Study ◽  
Subjective Data ◽  
Vehicle System ◽  
Vehicle Systems ◽  
Behavior Based ◽  
And Behavior

Usability of in-vehicle systems has become increasingly important for ease of operations and safety of driving. The user interface (UI) of in-vehicle systems is a critical focus of usability study. This paper studies how to use advanced computational, physiology- and behavior-based tools and methodologies to determine affective/emotional states and behavior of an individual in real time and in turn how to adapt the human-vehicle interaction to meet users' cognitive needs based on the real-time assessment. Specifically, we set up a set of physiological sensors that are capable of collecting EEG, facial EMG, skin conductance response, and respiration data and a set of motion sensing and tracking equipment that is capable of capturing eye ball movement and objects which the user is interacting with. All hardware components and software are integrated into an augmented sensor platform that can perform as “one coherent system” to enable multimodal data processing and information inference for context-aware analysis of emotional states and cognitive behavior based on the rough set inference engine. Meanwhile subjective data are also recorded for comparison. A usability study of in-vehicle system UI is shown to demonstrate the potential of the proposed methodology.

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