Alcohol-Triggered Accident Detection and Alert System with GSM,

GPS, ESP32 Integration

Sri Chakradhar Nossam

, Gopa Pulastya

, Rishi Anirudh Katakam

and Manju Khanna

Department of Computer Science and Engineering, Amrita School of Computing, Bengaluru, Karnataka, India

Keywords: Alcohol Detection, Accident Detection, GSM Communication, GPS Tracking, Road Safety, Drunk Driving,

Prevention, Real-Time Alerts, ThingSpeak, ESP32.

Abstract: The new invention of Alcohol-Triggered Accident Detection and Alert System with GSM, GPS, ESP32 and

utilizing ThingSpeak is a major step forward in road safety. This system uses an Arduino UNO board for data

collection and management, an MQ-3 Alcohol Detection Sensor for measuring the alcohol concentration in

the driver’s breath, a collision sensor for detecting an accident, a GPS module to track the vehicle’s location

in real-time, and a GSM module for immediate remote communication. In case the alcohol level rises beyond

the permissible limit or an accident happens, the system immediately initiates SMS notifications with the

location of the car to the emergency services and the specified authorities. Further, the ESP32 module sends

the data to ThingSpeak in real-time to monitor and analyze the driving behavior data. This interaction factor

does not only contribute to the real-time response but also contributes to the systematic data compilation for

better future road safety planning. Initial findings demonstrate the system’s high reliability and responsiveness,

promoting safe driving practices and supporting the goal of reducing road fatalities.

1 INTRODUCTION

Alcohol related incidents are considered to be one of

the main road traffic safety concerns that are always

associated with high risk of causing serious injuries

or even demise. Related with eating is drinking and

driving which reduces the capacity of observing

events on the road, reduces decision making capacity

and in- creases the possibility of an accident on the

road making it a vital issue to consider on the road.

The seventh season is devoted to such important

problems as the present days technological progress

can contribute to the improvement of many spheres

of human life including the questions connected with

road safety. In responding to this major issue, the

proposed Alcohol- Triggered Accident Detection and

Alert System with GSM, GPS, ESP32 Integration,

and ThingSpeak Integration is one of the promising

solutions to develop the road safety innovation.

This system involves the use of Arduino UNO

Board for acquiring the data and regulating the

processes of the system, MQ-3 Alcohol Detection

Sensor for determining the alcohol level in breath of

the driver, Collision Sensor for measuring an

accident, GPS Module for the real-time location and

GSM Module for immediate remote connectivity. In

the event that the levels of alcohol are high or an

accident occurred, the system immediately sends

Standard SMS messaged containing the vehicle’s

location to the police and other contacts as may be

tendered. Moreover, the collected data from the

sensors are sent using the ESP32 module to

ThingSpeak for real-time streaming and analysis of

the driving behavior.

This solution does not simply improve the ability

to respond to current incidents but also fosters the

gathering of data for future road safety planning.

From the perspective of meeting society's needs, the

system assists in enhancing safe driving and ensures

law violation penalties are meted out, reducing the

rate of fatal road accidents. Preliminary results prove

a considerably high level of the system’s reliability

and response speed; therefore, it can be stated that the

de-signed system could act as an efficient solution

aimed at enhancing the general standards of road

safety.

The structure of this paper is as follows: Section 2

conducts a comprehensive review of related works.

Section 3 tells us about the hardware requirements for

the model. Section 4 discusses the methodology used.

Nossam, S. C., Pulastya, G., Katakam, R. A. and Khanna, M.

Alcohol-Triggered Accident Detection and Alert System with GSM, GPS, ESP32 Integration.

DOI: 10.5220/0013577200004639

In Proceedings of the 2nd International Conference on Intelligent and Sustainable Power and Energy Systems (ISPES 2024), pages 82-89

ISBN: 978-989-758-756-6

Section 5 gives a brief idea of all the algorithms used.

Section 6 offers a thorough view of the results.

Finally, Section 7 concludes the paper with future

scope.

2 LITERATURE REVIEW

This section has conducted a thorough survey to

understand the recent approaches towards the alcohol

triggered accident detection system. The pr is about

implementing a system for the Accident Alert and

Vehicle Tracking using GPS and GSM. This system

informs the patient's people directly and gives them

the GPS instructions on how to get to the hospital. It

comprises of the crash, impact, piezoelectric sensors

and MEMS which are used to sense crush (Mounika.,

2021) When the accident is noticed, the

microcontroller sends a signal, which is transmitted

to the GPS unit, which in turn, is connected with the

GSM module, and so the driver is located and the

authorities and registered contacts are informed.

Besides, the system is also able to signal the vehicle's

current location via SMS when it is tampered with

(Vashista, B., 2021). The team developed "Vehicle

Accident Location Tracking System Using GSM and

GPS" informs the emergency services about accidents

which results in the dispatching of alerts to the

registered numbers through GPS and GSM modules

which are triggered by the impact sensors. This

system enables tracking in real-time, helps in the

cases of theft and other emergencies and therefore,

the first-aid services can respond fast and this might

even save some lives (Chandra., 2023).

The team demonstrated a GPS/GSM logger

system that is suitable for the detection of violations

of speeding and accidents. Through the use of an

impact sensor, piezoelectric transducer, and

accelerometer, the device finds the collisions and the

exact location of the crash by GPS. The GSM module

sends SMS alerts to predefined contacts; thus, the

contact can get the message and respond at once. The

system moreover identifies stolen cars, sending GPS,

latitude and longitude, and a Google Maps link via

SMS. This technology is designed to be a life-saver

by guaranteeing quick emergency reactions through

accurate location data (Kumar., 2023). The project is

about creating a Road Accident Alert System by the

means of GSM communication. This system, having

a GPS module, sends the messages for the driver and

the emergency services during the emergency

accidents. Decelerometers detect the collision signs,

and thus, the already set-up GPS location data is used

to alert the person. The school is designed to cut down

the dispatch times and to increase the location

accuracy, hence saving lives. Besides, the Green

Powered Fuel cells which are used in this system are

low maintenance cost and are easy to install in several

vehicles (Suhas., 2023).

The paper is about creating a Road Accident Alert

System by the means of GSM communication. This

system, having a GPS module, sends the messages for

the driver and the emergency services during the

emergency accidents. Decelerometers detect the

collision signs, and thus, the already set-up GPS

location data is used to alert the person. The school is

designed to cut down the dispatch times and to

increase the location accuracy, hence saving lives.

Besides, the Green Powered Fuel cells which are used

in this system are low maintenance cost and are easy

to install in several vehicles (Shakya, S., 2023). The

team has worked on a system that has a multipurpose

sensor system which includes the accelerators,

gyroscopes, and GPS that are able to detect and

prevent accidents and consequently, alert the

emergency services. Besides, it has a feature of

making a car driver aware of the lane departure and

collision warning which also provides useful data for

traffic safety studies and regulation improvements

(Patil., 2023).

The paper proposed a system which is based on

the use of the high-efficiency components which are

efficient in the implementation, and therefore, the

attempt to reduce the drunk driving accidents, in the

city is well implemented. Data collection is of great

significance, the one that enables us to get the results

of traffic safety surveys as well as the updates of the

system functionality used for policy formulation

(Pradeepkumar, G, 2023). The team underline the

ignition lock system that is activated by alcohol

sensors, thus, they manage to stop drunk drivers by

preventing the car from starting, if the driver's breath

contains alcohol above 0.08 percent. This system is a

very important tool of prevention that by means of

provoking the driver by eliminating the driver vision

it ensures that the car is immobilized when alcohol is

detected in the driver's breath (Vaishnavi, T, 2023).

The team analyze a pattern intended to decrease

the dangerous and significant increase in accidents

and fatalities as a result of drunk driving. The

implementation of this system will, of course, lead to

the saving of many lives and the prevention of many

injuries by successfully keeping the drunk drivers

away from the highway. Besides, the projected cost-

efficient, user friendliness and reliability of this

blockchain-based system could, in turn, decrease the

issues of drunk driving that are already present

(Suresh, S, 2023) The team studied a car system

Alcohol-Triggered Accident Detection and Alert System with GSM, GPS, ESP32 Integration

which is meant to boost driver security by means of

detection of the alcohol level. Their research

highlights a vital system development, the system that

is very small, self-installing and continuously sending

data through the blockchain to the blockchain itself.

This system not only reminds drivers about alcohol

levels but also promotes safe driving practices, thus,

making its simplicity, flexibility, and safety features

the foundation of credibility and reliability in

preventing drunk driving cases (Sinha, A., 2023).

Kumar et al. 's research is centered on increasing

sensor sensitivity by including silver nanoparticles,

which leads to an improvement in the performance of

diagnosing and monitoring contaminated water or

environments. The Fiber Bragg Grating is combined

with silver nanoparticles; therefore, it can be

improved in water quality sensors and it can be used

for other medical purposes as well as the environment

(Kumar, 2023). Ajagbe and his team focused on the

creation and evaluation of the same system that

makes it possible for us to recognize drunk driving

with the help of the virtue devices. Through this work,

the designers, the implementers will experience the

various stages ranging from the design stage to the

testing phase. The drastic changes that the system is

making in the road safety will be the main focus. The

virtual instruments usage in such a situation can be

seen as a true representation of the modern era and a

possibly efficient way of solving the problem of

drinking and driving. This type of research could

really be used to make a huge contribution in the field

of drunk driving detection systems and a new method

for alcohol level detection in drivers could be created.

The main concept of the research might be utilized in

the creation of efficient ways to ensure road safety

and reduce, which are widely spread among alcohol

impaired drivers (Ajagbe., 2023). Das et al, and

others developed a solution to prevent drunk driving

by measuring the blood alcohol level. This new

system improves road safety by blocking the impaired

drivers from starting their vehicles, thus, showing

how technology can be used to reduce the ones

requiring the risks of driving under the influence. The

study underlines the vitality of the introduction of

such innovative technologies to first of all, the safety

and then the reduction of the road accidents

influenced by the drunk drivers (Das., 2023) Upender

et al. suggested a system that can be used for accident

prevention involving Arduino and Eye Twitch and

Alcohol sensors which can detect the driver's

impairment in real-time. Through the observation of

disciplines like eyebrow movements and alcohol

concentration, the system intends to lessen the

number of road accidents by alerting the impaired

drivers and in the same time, it is promoting the safer

driving habits (Upender., 2023).

Hyder G et al, and the others, have introduced to

the world the Smart Automobile (SAM) application,

which is a combination of drowsiness detection,

alcohol monitoring, vital sign tracking, and lane-

based auto drive features that will in the end make the

highway safer and reduce the number of accidents.

The holistic idea which is the basis of the problem

(Hyder G, 2020). Tushaar et al, and others introduced

a new way of finding the unsafe driving behavior by

using the IoT technology. This system tracks the real

time parameters that let us identify the dangerous

driving practices, thus it sends the alerts and

interventions in time and thus the roads become safe.

Their studies in turn have a great impact on the

intelligent transportation systems, which is the

proposed way to solve the problems caused by unsafe

driving (Tushaar., 2022).

Chaganti et al, and others presents a new approach

that uses IoT technology for real-time accident

detection and prevention. Their method of work will

surely find out possible accidents, thus, the intelligent

systems for accident prevention and road safety

improvement become significant and should be

looked into. Moreover, their research will be a step

forward in the technological innovation to improve

road safety. (Chaganti., 2023).

Sethuraman et al, and others presents the way in

which IoT technology helps in the improvement of

vehicle monitoring and safety. The system of IoT

collects data on vehicle performance and driver

behavior in real time and then gives insights to

accident analysis and reconstruction. The research

proves that the usage of IoT-based solutions would be

very helpful in the improvement of vehicle safety and

accountability through advanced data collection and

analysis (Sethuraman., 2020). Kumar et al and others

introduces a new way using smart sensors and

communication systems to detect accidents in time.

The system they are working on is designed to

decrease the time of emergency response and to

improve accident management, hence, it is possible to

see the usage of smart systems for the transportation

safety and their findings show that there are

advancements in the accident detection and response

mechanisms for the safer roads (Kumar., 2021).

3 HARDWARE REQUIREMENTS

The developed model for alcohol-triggered accident

detection system and the hardware requirements

needed to develop the above model are as follows

ISPES 2024 - International Conference on Intelligent and Sustainable Power and Energy Systems

3.1 Arduino UNO

Arduino UNO, plays the role of the microprocessor to

manage data acquisition processes from alcohol

sensor, accelerometers, GPS and GSM modules. One

of them works with the sensors’ data input and

applying algorithms predetermined in advance to

measure alcohol levels and collision and other

collision sensor for engine locking system. If it

identifies an event such as alcohol level above the

threshold and collision, it initiates the correct

responses, including immobilizing the car’s engine

and sending out recall notifications.

Due to the modularity and flexibility of the de-

vice, it should be integrated into environments where

real-time response is crucial in safety-conscious

operations.

3.2 Alcohol Sensor

MQ-3 Alcohol Sensor is an integral element for

probing the breath alcohol content in the driver. This

is highly sensitive to the alcohol vapor and will give

correct measure of the percentage of ethanol in air.

Due to alcohol influence, when the level is over the

legal limit, the sensor delivers a signal to the Arduino

UNO to lock the engine and set an alarm. The ac-

curacy and dependable of this sensor are significant in

reducing the incidences of drunk driving thus im-

proving the safety of drivers on the roads.

3.3 Collision Sensor

Collision sensors measure abrupt changes in the

vehicle motion that signal crash occurrence. This con-

sists of continuously measuring the vehicle’s

acceleration in all three axes and triggering the

Arduino UNO whenever there is a large impact. This

it causes the system to invoke an emergency mode,

and which sends out notification messages with the

location of the vehicle. Also important is the ability of

the collision sensor to promptly and accurately alert of

the accident for prompt and appropriate reaction.

3.4 GSM Module

GSM Module 900A provides the system with the

ability to send out text messages to contact

emergency services and any persons that the system

may wish to inform in case of an emergency. There

are signs, for instance, an alcohol level beyond the

permissible limit or an accidental situation, the

module receives signals from the Arduino UNO to

issue alert messages. Such messages contain data

important for the responder, like the position of the

vehicle and the type of event that has occurred. The

GSM Module 900A’s connectivity hence guarantees

timely and efficient notification, emphasizing the key

importance of timely response to disasters and

emergencies.

3.5 GPS Module

GPS module plays a critical role where it assists in the

tracking of the real-time location of the vehicle. To

ascertain the exact latitude and longitude coordinates

of the car’s location, it utilizes satellites. If an incident

occurs the GPS module provides geolocation data that

can be incorporated as part of the message to be sent

to the outcry contacts. This comes in handy when it is

time to dispatch help because the GPS technology

makes it easier to pin point where the vehicle is

located thus boosting the chances of giving the

needed help on time.

3.6 ESP-32 Module

ESP-32 module is used for data transmission by

connecting the da sensor and ThingSpeak platform in

real time. It also maintains continuously monitoring

and which improves the function of the system. This

real-time data streaming feature makes it easy to

intervene and also gather more data in preparation for

additional road safety enhancement in the future.

4 PROPOSED METHODOLOGY

The proposed methodology for alcohol-triggered

accident detection system is showcased in Figure 1,

and explained in detail in the following subsections.

Figure 1: Proposed Methodology for Alcohol-Triggered

Accident Detection System.

Alcohol-Triggered Accident Detection and Alert System with GSM, GPS, ESP32 Integration

4.1 System Initialization

The process of powering up which involves activating

all the sensors which includes but not limited to the

Arduino UNO, MQ-3 Alcohol Detection Sensor,

Collision Sensor, Global Positioning System, Global

System for Mobile Communications, and ESP32.

Each one of them is set up to monitor and integrate

with other modules as required in its functioning. The

ESP32 module is interfaced to the local WIFI net-

work to ensure continuity in a data transfer process at

ThingSpeak.

4.2 Sensor Data Acquisition

The system thus, retrieves data from the MQ-3

Alcohol Detection Sensor and from the collision

sensor with the data concerning the level of alcohol

in the blood of the driver and possible accidents at any

given time. This data is then sent to the Arduino UNO

where the data is processed and appropriate action is

initiated based on the particular instruction. The

integration of such sensors offers confidence to the

tracking of important events that may require an

adequate and prompt response.

4.3 Accident Detection

With the collision sensor, the system constantly

checks and waits for a collision though it employs

other parameters which may include sharp jolts or

jerkiness that may point to an accident. In this case,

the sensor produces a signal that needs to be

interpreted by the Arduino UNO, in order to react

according to the set emergency response strategy.

This entails capturing the GPS coordinates of the

vehicle and send an SMS alert comprising of any

relevant information through the GSM module. These

instant notification procedures help in providing

quick response and support, which at times could be

fundamental in saving a life in emergency conditions.

4.4 Alcohol Detection

There is a presence of a sensor MQ-3 Alcohol

Detection Sensor which constantly measures the

driver’s breath alcohol level. In the case when the

sensor de- fines the level of alcohol secretion higher

than the threshold value, the Arduino UNO analyses

this data and triggers an alarm.

This alert makes the system to send an SMS text

message with the vehicle’s location to the emergency

services as well as the contacts that have been

identified. This mechanism ensures there is

immediate action is taken, which may likely reduce

cases of alcoholic induce mishaps on the road.

4.5 Data Transmission to ThingSpeak

Using ESP32

The ESP32 module is of particular importance when

it comes to sharing the sensor data with the

ThingSpeak cloud. Through Wi-Fi access, the ESP32

mean that data from the alcohol and collision sensors

is streamed in real-time to the local WIFI network.

This constant transfer of data helps track the behavior

and patterns of drivers on the roads continuously, and

therefore used to address upcoming future road safety

strategies.

Use of ThingSpeak also helps augment the

capacity of the existing system and enhance the

effectiveness of giving a better outlook on the risky

regions of the roads.

5 ALGORITHMS USED

5.1 GSM Module

Initialize GSM 900A() check network availability()

send(”AT+CMGF=1”) if accident detected() or

alcohol detected(): coordinates = get GPS

coordinates() send SMS(”Alert! Location: ” +

coordinates).

5.2 GPS Module

Initialize GPS() while not gps signal available():

check gps signal() if accident detected() or alco- hol

detected(): coordinates = get GPS coordinates()

format and send alert(coordinates).

5.3 ESP32 Module

Initialize ESP32() connect to WIFI(ssid, pass- word)

initialize ThingSpeak() while True: if accident

detected() or alcohol detected(): coordinate = get GPS

coordinates() send data to ThingSpeak(coordinates,

sensor data) delay(interval).

6 RESULTS AND DISCUSSION

The developed model proves that both alcohol

detection and collision sensing are integrated

highlights that there has been a positive advancement

on im- proving road safety. The system blocks the

ISPES 2024 - International Conference on Intelligent and Sustainable Power and Energy Systems

engine of the vehicle when it analyses alcohol

concentrations which is unlawful. In the case of

presence of alcohol, the GSM and GPS are switched

on by the system as described above. The GSM

module makes a request for the longitude and latitude

of the vehicle which is implemented through the

Global positioning system (GPS). This will ensure

that any information on the real-time tracking

produced by the tracking device is conveyed to the

concerned parties on the intended lo- cation of the

vehicle.

In practical tests, the device of detecting alcohol

gave the correct result in raising the higher amount of

alcohol in the driver’s breath which activated the lock

of engine immediately. This automatic response helps

to reduce the potential for accidents that stem from

driving while under the influence. Then the GSM

module electronically transmitted, through an SMS,

the position of the vehicle to the pertinent individuals.

This capability entails that operations of the

emergency services can be coordinated efficiently

and responded to as they happen thus enhancing cost

effective solution or prevention or incidents.

Moreover, in the context of simulations, the

collision detection sensor was also implemented in a

rather effective manner. The movement and impact

sensors sensed abrupt variations to the motion of the

vehicle and the active emergency procedure was

launched. This included forwarding details of the

accident and the current location of the car to the

contacts that the car owner had listed. Another feature

of the system is that it can identify not only the

alcohol level but also collisions, thereby expanding

the potential for improving road safety.

Figure 2: Messages sent by the Model when the accident

and crash is detected in the car

In the Fig-2, the bottom figure shows the message

when the Alcohol is detected and if it is more than the

legal limits this is sent by the model. In the top shows

the message when the collision sensor gets reading

when it is being collided the message is sent by the

model.

Moreover, in the context of simulations, the

collision detection sensor was also implemented in a

rather effective manner. The movement and impact

sensors sensed abrupt variations to the motion of the

vehicle and the active emergency procedure was

launched. This included forwarding details of the

accident and the current location of the car to the

contacts that the car owner had listed. Another feature

of the system is that it can identify not only the

alcohol level but also collisions, thereby expanding

the potential for improving road safety.

In the Figure 2, the bottom figure shows the

message when the Alcohol is detected and if it is more

than the legal limits this is sent by the model. In the

top shows the message when the collision sensor gets

reading when it is being collided the message is sent

by the model. Moreover, the ESP32 module has a

significant function of transmitting data to

ThingSpeak for immediate streaming and analysis of

the driving behavior. The ESP32 module has been

configured to upload data to the ThingSpeak by

connecting to the local WIFI network; thus, yielding

a thorough analytical insight. The integration with

other types of data that focus on cars enables the

purpose of identifying the most common driving

patterns and the prevention of accidents in the future.

Figure 3: The Analysis of the readings of MQ-3 Alcohol

Sensor in ThingSpeak

Fig.3, displays the graph related to alcohol

readings over time as recorded by the system and

trans- mitted to ThingSpeak. Notably, there are

fluctuations in the alcohol levels throughout the day.

Around 12:00, there is a significant peak in alcohol

concentration, reaching above 0.1 (100 parts per

million), indicating a potentially dangerous level of

alcohol in the driver’s breath. Following this peak, the

readings show some variation but remain relatively

high until approximately 16:00.

These fluctuations highlight the system’s ability

to continuously monitor and record real-time alcohol

levels, ensuring timely alerts and interventions when

dangerous levels are detected. When the model is

compared, our system can be more effective than

other studies in terms of alcohol detection while

integrating it with accident detection, real-time car

tracking and notification and the usage of ESP32

along with the integration of ThingSpeak makes our

system unique. Systems developed earlier by the

authors were confined to giving out alerts in case of

accidental happenings using GPS as well as GSM,

without addressing the problem of alcohol detection

as a prelude to these alerts. Further enhancing the

Alcohol-Triggered Accident Detection and Alert System with GSM, GPS, ESP32 Integration

idea, all these functionalities can be integrated into

one system which is effectively tackling various

aspects of road safety at once.

Thus, the Alcohol-Triggered Accident Detection

and Alert System corresponds to the development of

the road security topic. The features of the system,

such as alcohol level identification, collision

detection and, in real-time data reporting specifically

target qualities that may lead to reckless driving

accidents. About the future, more advancements

could be seen in improving the sensors, observing the

drivers’ activities in real time, and connecting the

smart car system with traffic signals. These

enhancements would further encourage greater safety

for the users and help in ticket prevention for unsafe

driving leading to safer traffic flow.

7 CONCLUSION AND FUTURE

SCOPE

In Conclusion, The Alcohol-Triggered Accident

Detection and Alert System offers an advancement in

road safety by incorporating alcohol detection,

collision sensing capabilities, GPS tracking, GSM

connectivity, and real-time data transmission through

the ESP32 module and ThingSpeak. This

sophisticated system prevents the car engine from

operating when there is presence of alcohol above the

defined limit and the vehicle can also phone the

emergency services with position of the vehicle hence

the probability of having an accident due to presence

of alcohol is al- most negligible. This makes a

significant difference in real-world circumstances

and ensures higher levels of accuracy and reliability;

improving safety by pre- venting hazardous events

before they happen.

Further developments may turn to have machine

learning mechanisms for the analysis of the sensor

data and to understand the driving patterns to better

identify risks and warn a driver in real time. Machine

learning approaches could also improve the

effectiveness of alcohol identification and collision

sensing. These improvements would improve a

personal safety and increase the rate of preventable

events and general road safety.

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Alcohol-Triggered Accident Detection and Alert System with GSM, GPS, ESP32 Integration