Novel Drunken Driving Detection and Prevention Models Using Internet of Things

Sandeep, K V; Ravikumar, Ponnam; Ranjith, Sura

doi:10.1109/icrteect.2017.38

Cited by 35 publications

(11 citation statements)

References 3 publications

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“…The system uses four MQ-3 sensors to detect alcohol activity, disables the vehicle ignition and sends location alerts to the caretakers of the driver. A similar system based on Raspberry Pi, heart rate sensor, camera and alcohol sensor was proposed in [6]. A fleet monitoring system which can provide on-demand location updates and drunken driving alerts to the owners of fleet services was designed and demonstrated in [7].…”

Section: Existing Methodsmentioning

confidence: 99%

NodeMCU and IoT-based Safety and Security Ecosystem for Heavy Vehicles

Patwari¹

2020

IJETER

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This paper explains the design and implementation of a safety system for heavy vehicles. A small-scale embedded system based on Node MicroController Unit (NodeMCU) is proposed and prototyped here. Peripherals such as Global Positioning System (GPS) receiver (for location updates), tilt sensor (for accident detection), gas sensor (for alcohol activity detection), RFID tags and reader (to monitor entry and exit of vehicles in restricted areas), a touch sensor (to indicate an emergency) etc., have been used in this system. A major improvement over existing accident alert systems is that the proposed design makes use of ultrasonic sensors to indicate over-height of the vehicles approaching underpasses (tunnels or rail over road bridges) on highways. A sound alarm is triggered if the vehicle's height is more than the entry point of the underpass so that the driver can slow down the vehicle. A working prototype of the overall system has been implemented and tested successfully. The data gathered by all sensors is sent to the cloud using the Message Queuing Telemetry Transport (MQTT) protocol and the user can monitor all the updates using his/her android smartphone.

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Section: Existing Methodsmentioning

confidence: 99%

NodeMCU and IoT-based Safety and Security Ecosystem for Heavy Vehicles

Patwari¹

2020

IJETER

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“…In paper [ 13 ], The researchers provided a strategy for detecting accidents that are primarily caused by those who had consumed alcohol while driving. According to the author, different sensors, including alcohol sensors, heartbeat sensors, and touch sensor interfaces with a Raspberry Pi, were used in this system.…”

Section: Related Workmentioning

confidence: 99%

5G and IoT Based Reporting and Accident Detection (RAD) System to Deliver First Aid Box Using Unmanned Aerial Vehicle

Alkinani

Almazroi

Jhanjhi

et al. 2021

Sensors

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Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics. Due to the rapid increase in population, vehicle numbers are increasing, resulting in a large number of road accidents. The majority of the time, casualties are not appropriately discovered and reported to hospitals and relatives. This lack of rapid care and first aid might result in life loss in a matter of minutes. To address all of these challenges, an intelligent system is necessary. Although several information communication technologies (ICT)-based solutions for accident detection and rescue operations have been proposed, these solutions are not compatible with all vehicles and are also costly. Therefore, we proposed a reporting and accident detection system (RAD) for a smart city that is compatible with any vehicle and less expensive. Our strategy aims to improve the transportation system at a low cost. In this context, we developed an android application that collects data related to sound, gravitational force, pressure, speed, and location of the accident from the smartphone. The value of speed helps to improve the accident detection accuracy. The collected information is further processed for accident identification. Additionally, a navigation system is designed to inform the relatives, police station, and the nearest hospital. The hospital dispatches UAV (i.e., drone with first aid box) and ambulance to the accident spot. The actual dataset from the Road Safety Open Repository is used for results generation through simulation. The proposed scheme shows promising results in terms of accuracy and response time as compared to existing techniques.

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“…There will always be things that can go wrong, occurrences which cannot be anticipated. Prevention of accidents caused by drunken driving has been studied based on the internet of things concept using Raspberry Pi 3 model B (Sandeep et al, 2017). In the present proposal it has been attempted to enhance the available methods and have an effective system to prevent accidents due to driver's fatigue caused by adverse influences.…”

Section: Success Of the Proposed Systemmentioning

confidence: 99%