Hybrid Social Force-Fuzzy Logic Evacuation Simulation Model for Multiple Exits

Technological advancements have widely contributed to navigation aids. However, their large-scale adaptation for navigation solutions for visually impaired people haven’t been realized yet. Less participation of the visually impaired subject produces a designer-oriented navigation system which overshadows consumer necessity. The outcome results in trust and safety issues, hindering the navigation aids from really contribute to the safety of the targeted end user. This study categorizes electronic travel aids (ETAs) based on experimental evaluations, highlights the designer-centred development of navigation aids with insufficient participation of the visual impaired community. First the research breaks down the methodologies to achieve navigation, followed by categorization of the test and experimentation done to evaluate the systems and ranks it by maturity order. From 70 selected research articles, 51.4% accounts for simulation evaluation, 24.3% involve blindfolded-sighted humans, 22.9% involve visually impaired people and only 1.4% makes it into production and commercialization. Our systematic review offers a bird’s eye view on ETA development and evaluation and contributes to construction of navigational aids which really impact the target group of visually impaired people.

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“…from the visually impaired person's point of view. Other than moving towards destination, wayfinding also includes the action of evacuation [14].…”

Section: Wayfindingmentioning

confidence: 99%

Methodologies and evaluation of electronic travel aids for the visually impaired people: a review

et al. 2021

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“…For safety purposes during real data collection, theoretical analysis via simulations are normally conducted to analyze unforeseen problems during the evacuation. However, the problem with simulation is that it lacks immersion and understanding of real scenarios causing varying reactions and behaviors and thus affecting the result of the studies [7][8][9]. Another fundamental problem during evacuation is unfamiliarity with the structure of the building, which slows down movement to the exit.…”

Section: Introductionmentioning

confidence: 99%

Immersive Augmented Reality System to Study the Efficiency of Dynamic Exit Signage

Ibrahim

Kamaruddin

Wahid

2022

IIUMEJ

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Every year, many disasters occur to buildings causing their destruction and leading to huge casualties. One way of preventing casualties is by evacuation drill activity. Although accurate evacuation drills could enhance the efficiency of the process during the real event, these drills are not fully effective because participants miss the sense of being stressed or under pressure while in action. Several gaming concepts have been introduced to train the participants on how to cope with and evacuate effectively during an emergency. For instance, Augmented Reality (AR) and Virtual Reality (VR) interfaces could provide virtual content to enhance the effectiveness of evacuation drills. However, accurate representation of different evacuation scenarios and its impact analysis during emergency using the above technologies are still debatable, mainly due to immersion quality. Thus, this study proposes an Immersive Augmented Reality (IAR) application that is mainly the amalgamation of AR and VR in realizing fast and safe evacuation during on-site building emergencies. A virtual dynamic exit signage system is also developed in the proposed “Smart Evacuation application“. This work evaluated the efficiency of a virtual dynamic exit signage and also a proposed “Smart Evacuation“ system by analysing on-site emergency evacuation processes. By setting up various scenarios imitating real life disasters, this research analysed the time taken and level of stress of the occupants during the evacuation of a chosen site. The proposed “Smart Evacution“ achieved 33.82% better perfomance compared to normal evacuation thus indicating a faster and safer evacuation. ABSTRAK: Secara statistik, kebanyakan bencana kemusnahan bangunan yang berlaku setiap tahun telah menyebabkan kerugian besar. Salah satu cara bagi mengelak kejadian ini adalah melalui aktiviti latih tubi evakuasi. Walaupun latih tubi evakuasi ini dapat menambah proses kecekapan semasa kejadian sebenar, latih tubi ini tidak benar-benar berkesan kerana peserta kurang mendalami perasaan tertekan atau di bawah tekanan semasa kejadian. Pelbagai konsep permainan telah diperkenalkan bagi melatih peserta bagaimana perlu bertindak dan evakuasi secara efektif semasa kecemasan. Sebagai contoh, antarmuka Realiti Terimbuh (AR) dan Realiti Maya (VR) mungkin dapat menghasilkan simulasi secara maya bagi menambah keberkesanan latih tubi evakuasi. Walau bagaimanapun, ketepatan representasi pelbagai senario evakuasi dan analisis tekanan semasa kecemasan menggunakan teknik-teknik di atas adalah masih boleh dipertikaikan terutama kerana kualiti kedalamannya. Oleh itu, kajian ini mencadangkan aplikasi Realiti Terimbuh Mendalam (IAR) di mana tumpuan adalah pada kombinasi AR dan VR dibuat dengan secara evakuasi pantas dan selamat semasa kecemasan pada bangunan kejadian. Sistem maya penunjuk arah keluar dinamik turut dicipta dalam “Aplikasi Evakuasi Pintar” yang dicadangkan ini. Kajian ini menilai keberkesanan sistem maya penunjuk arah keluar secara dinamik dan juga sistem “Evakuasi Pintar” dengan menganalisa proses evakuasi kecemasan pada tempat kejadian. Dengan mengadakan pelbagai jenis senario dan meniru bencana sebenar, kajian ini menganalisa masa yang diambil dan tahap tekanan penghuni bangunan semasa proses evakuasi berlaku pada tapak pilihan. “Evakuasi Pintar” ini mencapai 33.82% keberkesanan pada prestasi berbanding evakuasi biasa. Ia membuktikan proses evakuasi ini lebih pantas dan selamat.

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“…The LiDAR sensor is robust to illumination variation [6], and can be used to obtain the transformation matrix between 2D coordinate system and 3D model of the scene [7]. Its detection range is also comparatively higher in accuracy and provides better reliability when compared to stereo methods [8,9].…”

Section: Introductionmentioning

confidence: 99%

Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud

et al. 2021

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Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed.

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Hybrid Social Force-Fuzzy Logic Evacuation Simulation Model for Multiple Exits

Cited by 4 publications

References 9 publications

Methodologies and evaluation of electronic travel aids for the visually impaired people: a review

Methodologies and evaluation of electronic travel aids for the visually impaired people: a review

Immersive Augmented Reality System to Study the Efficiency of Dynamic Exit Signage

Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud

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