IoT-enabled Drowsiness Driver Safety Alert System with Real-Time Monitoring Using Integrated Sensors Technology

• URL: http://arxiv.org/abs/2502.00347v1
• Date: Sat, 01 Feb 2025 07:11:13 GMT
• Title: IoT-enabled Drowsiness Driver Safety Alert System with Real-Time Monitoring Using Integrated Sensors Technology
• Authors: Bakhtiar Muiz, Abdul Hasib, Md. Faishal Ahmed, Abdullah Al Zubaer, Rakib Hossen, Mst Deloara Khushi, Anichur Rahman,
• Abstract summary: This paper aims to create an Internet of Things (IoT)-enabled Drowsiness Driver Safety Alert System with Real-Time Monitoring Using Integrated Sensors Technology. The system features an alcohol sensor and an IR sensor for detecting alcohol presence and monitoring driver eye movements. Data from the IR sensor is transmitted to a mobile phone via Bluetooth for real-time monitoring and alerts.
• Score: 0.0
• License:
• Abstract: Significant losses in terms of life and property occur from road traffic accidents, which are often caused by drunk and drowsy drivers. Reducing accidents requires effective detection of alcohol impairment and drowsiness as well as real-time driver monitoring. This paper aims to create an Internet of Things (IoT)--enabled Drowsiness Driver Safety Alert System with Real-Time Monitoring Using Integrated Sensors Technology. The system features an alcohol sensor and an IR sensor for detecting alcohol presence and monitoring driver eye movements, respectively. Upon detecting alcohol, alarms and warning lights are activated, the vehicle speed is progressively reduced, and the motor stops within ten to fifteen seconds if the alcohol presence persists. The IR sensor monitors prolonged eye closure, triggering alerts, or automatic vehicle stoppage to prevent accidents caused by drowsiness. Data from the IR sensor is transmitted to a mobile phone via Bluetooth for real-time monitoring and alerts. By identifying driver alcoholism and drowsiness, this system seeks to reduce accidents and save lives by providing safer transportation.

Related papers

• Real-time accident detection and physiological signal monitoring to enhance motorbike safety and emergency response [0.35337216626844875]
  Rapid urbanization and improved living standards have led to a substantial increase in the number of vehicles on the road. Motorbike accidents pose a particularly high risk, often resulting in serious injuries or deaths. We propose a novel automatic detection and notification system specifically designed for motorbike accidents.
  arXiv  Detail & Related papers  (2024-03-28T01:41:31Z)
• Vision-based Analysis of Driver Activity and Driving Performance Under the Influence of Alcohol [1.2094859111770522]
  30% of all traffic crash fatalities in the U.S. involve drunk drivers. Driving impairment can be monitored through active use of sensors. More passive and robust mechanism of sensing may allow for wider adoption.
  arXiv  Detail & Related papers  (2023-09-14T20:34:30Z)
• Driver Drowsiness Detection System: An Approach By Machine Learning Application [0.0]
  A million people worldwide die each year due to traffic accident injuries. drowsiness becomes the main principle for to increase in the number of road accidents. This paper focus to resolve the problem of drowsiness detection with an accuracy of 80%.
  arXiv  Detail & Related papers  (2023-03-11T05:05:36Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• Modelling and Detection of Driver's Fatigue using Ontology [60.090278944561184]
  Road accidents are the eight leading cause of death all over the world. Various factors cause driver's fatigue. Ontological knowledge and rules for driver fatigue detection are to be integrated into an intelligent system.
  arXiv  Detail & Related papers  (2022-08-31T08:42:28Z)
• In-vehicle alertness monitoring for older adults [63.359033532099204]
  We present a system for in-vehicle alertness monitoring for older adults. We implemented a prototype traveler monitoring system and evaluated the alertness detection algorithm on ten older adults (70 years and older. This study is the first of its kind for a hitherto under-studied population and has implications for future work on algorithm development and system design through participatory methods.
  arXiv  Detail & Related papers  (2022-08-17T06:07:37Z)
• Drivers' attention detection: a systematic literature review [62.997667081978825]
  Many factors can contribute to distractions while driving, since objects or events to physiological conditions, as drowsiness and fatigue, do not allow the driver to stay attentive. The technological progress allowed the development and application of many solutions to detect the attention in real situations. Our work presents a Systematic Literature Review of the methods and criteria used to detect attention of drivers at the wheel.
  arXiv  Detail & Related papers  (2022-04-06T11:36:40Z)
• The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention [44.94118128276982]
  A smart vehicle should be able to monitor the actions and behaviors of the human driver to provide critical warnings or intervene when necessary. Recent advancements in deep learning and computer vision have shown great promise in monitoring human behaviors and activities. A vast amount of in-domain data is required to train models that provide high performance in predicting driving related tasks.
  arXiv  Detail & Related papers  (2020-12-23T16:37:17Z)
• Computer Vision based Accident Detection for Autonomous Vehicles [0.0]
  We propose a novel support system for self-driving cars that detects vehicular accidents through a dashboard camera. The framework has been tested on a custom dataset of dashcam footage and achieves a high accident detection rate while maintaining a low false alarm rate.
  arXiv  Detail & Related papers  (2020-12-20T08:51:10Z)
• Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm [8.356765961526955]
  This work is to extend the driver drowsiness detection in vehicles using signals of a driver monitoring camera. For this purpose, 35 features related to the driver's eye blinking behavior and head movements are extracted in driving simulator experiments. A concluding analysis of the best performing feature sets yields valuable insights about the influence of drowsiness on the driver's blink behavior and head movements.
  arXiv  Detail & Related papers  (2020-09-28T12:37:38Z)
• A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis [164.93739293097605]
  EEG is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications.
  arXiv  Detail & Related papers  (2020-08-25T18:21:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.