Advanced Driver's Alarms System through Multi-agent Paradigm

Sipele, Óscar; Zamora, Víctor; Ledezma, Agapito; Sanchís, Araceli

doi:10.1109/icite.2018.8492600

Cited by 6 publications

(7 citation statements)

References 145 publications

(164 reference statements)

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“…Furthermore, the model can be associated with the police departments to fine violators and identify drivers' actions in case of accidents. The model can also be integrated with face recognition and alarm systems [60,61] capabilities that can allow the model to be used in a wide range of applications, such as driver authentication and theft prevention. Finally, the model can be developed and used in autonomous vehicles to detect critical conditions or situations that might endanger the driver's health and safety, such as strokes, heart attacks, and other sicknesses.…”

Section: Discussionmentioning

confidence: 99%

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

Aljasim

Kashef

2022

Sensors

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The increasing number of car accidents is a significant issue in current transportation systems. According to the World Health Organization (WHO), road accidents are the eighth highest top cause of death around the world. More than 80% of road accidents are caused by distracted driving, such as using a mobile phone, talking to passengers, and smoking. A lot of efforts have been made to tackle the problem of driver distraction; however, no optimal solution is provided. A practical approach to solving this problem is implementing quantitative measures for driver activities and designing a classification system that detects distracting actions. In this paper, we have implemented a portfolio of various ensemble deep learning models that have been proven to efficiently classify driver distracted actions and provide an in-car recommendation to minimize the level of distractions and increase in-car awareness for improved safety. This paper proposes E2DR, a new scalable model that uses stacking ensemble methods to combine two or more deep learning models to improve accuracy, enhance generalization, and reduce overfitting, with real-time recommendations. The highest performing E2DR variant, which included the ResNet50 and VGG16 models, achieved a test accuracy of 92% as applied to state-of-the-art datasets, including the State Farm Distracted Drivers dataset, using novel data splitting strategies.

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Section: Discussionmentioning

confidence: 99%

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

Aljasim

Kashef

2022

Sensors

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“…The drowsiness detection system developed in this work is part of a driver-based ADAS system [25,26], with two important restrictions: early detection and minimization of the number of false positives. The idea is that the system will warn the driver only in real cases of fatigue, to avoid false positives, which would cause boredom in the driver, causing them to turn off the ADAS, without executing the rest of the functionalities.…”

Section: Methodsmentioning

confidence: 99%

Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images

et al. 2022

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This work presents the development of an ADAS (advanced driving assistance system) focused on driver drowsiness detection, whose objective is to alert drivers of their drowsy state to avoid road traffic accidents. In a driving environment, it is necessary that fatigue detection is performed in a non-intrusive way, and that the driver is not bothered with alarms when he or she is not drowsy. Our approach to this open problem uses sequences of images that are 60 s long and are recorded in such a way that the subject’s face is visible. To detect whether the driver shows symptoms of drowsiness or not, two alternative solutions are developed, focusing on the minimization of false positives. The first alternative uses a recurrent and convolutional neural network, while the second one uses deep learning techniques to extract numeric features from images, which are introduced into a fuzzy logic-based system afterwards. The accuracy obtained by both systems is similar: around 65% accuracy over training data, and 60% accuracy on test data. However, the fuzzy logic-based system stands out because it avoids raising false alarms and reaches a specificity (proportion of videos in which the driver is not drowsy that are correctly classified) of 93%. Although the obtained results do not achieve very satisfactory rates, the proposals presented in this work are promising and can be considered a solid baseline for future works.

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“…Para a análise e selec ¸ão dos estudos primários, foram utilizadas como fontes de dados as bibliotecas digitais: ACM Digital Library, IEEE Xplore Digital Library e ScienceDirect, nas quais foram executadas as strings de busca no título, resumo e palavras-chaves, no período de 2010 a 2021, conforme Figura 1. Como resultado da análise, foram selecionados os trabalhos de [SIPELE et al 2018 [SIPELE et al 2018] propõe uma arquitetura baseada no paradigma multiagente para projetar um sistema assistente de direc ¸ão avanc ¸ada através da fusão de dados. O objetivo principal é projetar um sistema hierárquico capaz de gerenciar o processo de aquisic ¸ão de conhecimento de todos os aspectos envolvidos na cena de conduc ¸ão, como a ambiente, bem como o comportamento e estado do condutor, fornecendo suporte para construir e testar modelos de raciocínio.…”

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Multiagent Prototype with Sensors Fusion and Fuzzy Logic for Assessment of Security Systems Availability

Lira,

Fernández,

Campos

2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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Este trabalho utiliza inteligência computacional para mitigação do problema de monitoramento da disponibilidade de sistemas de segurança (videovigilância, detecção de alarme, controle de acesso) em ambientes físicos (bancos, indústrias, hospitais). Para tanto, foi proposto um sistema com três agentes inteligentes: fusão de dados; inferência fuzzy para criação do Índice de Vulnerabilidade de Segurança Física (IVSF); monitoramento e ações no ambiente físico. Na validação do modelo, foi implementado protótipo com dispositivos e protocolos de comunicação baseados em Internet das Coisas (IoT). Os resultados do sistema proposto foram considerados satisfatórios, mostrando-se viável para implementação em ambiente físico real.

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Advanced Driver's Alarms System through Multi-agent Paradigm

Abstract: because I learned a lot nearby all of you and I feel grateful to have been surrounded by great people who never refused to help me when I needed it.My thanks and appreciation also go to my colleagues

Cited by 6 publications

References 145 publications

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images

Multiagent Prototype with Sensors Fusion and Fuzzy Logic for Assessment of Security Systems Availability

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