Chain Accident Prevention by Applying Automatic Braking via

Vehicle to Vehicle Communication

Sowmya P.

, Prajjwal Kumar

, Kumar Pratyush

, Sachin Chaudhary

and Sachin Srivastava

Department of Electronics and Communication, Dayananda Sagar College of Engineering, Bengaluru, India

Keywords: Vehicular Ad-Hoc Networks (VANETs), Collision Detection, Zigbee Communication, Arduino UNO,

ESP32, UV Sensors, ADXL345 Accelerometer, Light Dependent Resistor (LDR), Telegram-Based

Notifications, Internet of Things (IoT), Smart Transportation, Emergency Response Systems, Road Safety

Systems, Real- Time Data Exchange, Centralized Communication Hub, Low-Latency Communication,

Automated Braking.

Abstract: Chain collisions are significant causes of traffic-related accidents and casualties as well as property damage,

resulting largely from delayed driver reaction time and inadequate communication between various vehicles.

This research shall design an IoT-based system equipped with V2V communication capability and automatic

braking to preclude such accidents. Implementing the system by taking advantage of cost-effective materials

such as Arduino UNO microcontrollers, Zigbee communication modules, and UV sensors ensures real-time

danger detection and response. A centralized hub powered byESP32 facilitates emergency notifications via

the Telegram platform, hence enhancing coordination with stakeholders. Other features include glare

reduction using LDR sensors and real-time environmental monitoring to enhance night-time visibility and

driving safety. Combining these technologies, the system reduces chain collisions, shortens response times in

emergencies, and improves overall traffic safety. This research points out the enormous potential of

integrating IoT and V2V technologies to shape smarter, safer transportation networks.

1 INTRODUCTION

Chain collisions on roads and highways are a

significant cause of site visitors’ accidents

internationally, often resulting in excessive injuries,

fatalities, and full-size property harm. These injuries

typically occur due to behind schedule driver

reactions, bad visibility, and the absence of effective

communication be- tween vehicles. As the need for

safer and smarter transportation systems grows,

modern answers that leverage emerging technologies

are vital to addressing those challenges. This study

introduces an IoT-based machine that makes use of

Vehicle-to-Vehicle (V2V) conversation and

automatic braking mechanisms to save.

https://orcid.org/0000-0003-0211-6130

https://orcid.org/0009-0003-9188-1842

https://orcid.org/0009-0003-7281-6586

https://orcid.org/0009-0006-3860-3159

https://orcid.org/0009-0006-5004-0475

Additionally, it contains a centralized

communication hub that transmits critical indicators,

consisting of accident notifications warnings, to

nearby automobiles and emergency responders

through Telegram. By combining hardware and

software components, the system ensures seamless

communication and reliable responses to

emergencies

At the core of the system are UV sensors for

obstacle detection and ADXL345 accelerometers for

impact sensing, which monitor the vehicle’s

environment continuously. Zigbee communication

modules enable low-latency data exchange be- tween

vehicles, facilitating rapid hazard detection and

response. Arduino UNO microcontrollers process

P., S., Kumar, P., Pratyush, K., Chaudhary, S. and Srivastava, S.

Chain Accident Prevention by Applying Automatic Braking via Vehicle to Vehicle Communication.

DOI: 10.5220/0013652500004639

In Proceedings of the 2nd International Conference on Intelligent and Sustainable Power and Energy Systems (ISPES 2024), pages 157-163

ISBN: 978-989-758-756-6

157

sensor data to make decisions in real time, while LDR

sensors dynamically adjust headlight intensity to

reduce glare during nighttime driving. The

centralized hub, equipped with WiFi connectivity,

relays alerts to a Telegram based notification system,

ensuring timely communication with drivers and

authorities. The system aims to reduce the likelihood

of rear-end and chain collisions by enabling real-time

hazard detection and intervention. It additionally

enhances driving safety with the aid of enhancing

midnight visibility and providing reliable emergency

communication. By addressing those important gaps

in current road protection measures, the proposed

system gives a complete technique to mitigate

accidents, enhance response times. Early checks of

the system dis- play its potential to noticeably

improve road protection, marking a leap forward in

the development of intelligent transportation

networks.

This research paper details the design,

implementation, and testing of the proposed system,

high- lighting its contribution to advancing IoT-based

road safety solutions. Through this integration of

sensors, communication modules, and centralized

control, the project lays the foundation for smarter,

safer roads in the future.

2 BACKGROUND AND

RELATED WORK

2.1 Background

Chain collisions are among the most hazardous types

of traffic accidents, often resulting in multiple

fatalities and extensive property damage. These

accidents typically occur due to a cascade of delayed

reactions among drivers, particularly in high-speed or

congested traffic scenarios. Factors such as poor

visibility, insufficient warning systems, and the lack

of Real Time communication be- tween vehicles

exacerbate these events. As mod- ern transportation

systems evolve, there may be a pressing need for

technological advancements that no longer only

beautify motive force protection but also cope with

those systemic challenges comprehensively. Vehicle-

to-Vehicle (V2V) verbal exchange has emerged as a

promising solution for enhancing avenue protection.

By permitting cars to percent- age real-time records

about their environment and riding situations, V2V

communication structures can offer timely signals to

drivers or even trigger computerized responses,

consisting of braking or lane adjustments. The

integration of Internet of Things (IoT) technologies in

addition enhances the ability of such systems, bearing

in mind seam- less communication, advanced threat

detection, and centralized manipulation of safety

mechanisms.

Despite significant advancements, current

implementations face limitations. Most existing

solutions rely heavily on expensive hardware, such as

LIDAR or radar systems, which are not eco-

nomically feasible for widespread adoption.

Additionally, issues such as connectivity disruptions,

latency in data transmission, and environmental

sensitivity of sensors remain significant hurdles.

Addressing these challenges requires a cost effective,

robust, and scalable solution that integrates affordable

hardware with reliable communication protocols.

2.2 Related Work

Several research efforts have focused on improving

road safety through V2V communication and

automated systems. Studies on Adaptive Cruise

Control (ACC) and Lane Departure Warning Systems

(LDWS) have demonstrated the benefits of

automated systems in reducing accidents. ACC

systems help maintain safe distances between

vehicles, while LDWS systems alert drivers to un-

intended lane departures. However, these systems are

often standalone and lack the ability to communicate

with other vehicles in real-time, limiting their

effectiveness in preventing chain collisions.

Recent research has explored the use of Zig- bee

communication modules for V2V interaction. Zigbee

offers low-latency and energy- efficient data

exchange, making it suitable for real-time

communication in vehicular networks. Researchers

have also investigated the use of UV sensors for

obstacle detection and accelerometers for impact

sensing, which provide critical data for hazard

identification. However, these systems often lack

integration with centralized hubs for emergency

communication, reducing their scope of applicability.

IoT-enabled systems have further expanded the

possibilities for road safety. Projects utilizing WiFi

and mobile networks to transmit accident

notifications to authorities have shown promise in

improving emergency response times. The use of

messaging platforms, such as Telegram, for real- time

alerts adds another layer of efficiency and

convenience. Despite these advancements, the

challenge of creating a cohesive system that combines

hazard detection, automated response and emergency

communication remains largely unaddressed.

The system proposed in this research builds upon

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

158

these advancements by integrating affordable and

reliable hardware, such as Arduino UNO

microcontrollers, UV sensors, and Zigbee modules,

with a centralized communication hub. Un- like

standalone systems, this approach ensures seamless

coordination between vehicles, enabling rapid

response to hazards and reducing the likelihood of

chain collisions. By addressing the limitations of

existing solutions, this project con- tributes to the

development of safer and smarter transportation

networks.

3 SYSTEM DESIGN

The proposed system integrates Arduino UNO as the

primary microcontroller for vehicles and ESP32 for

the centralized communication hub. This design

ensures cost-effective and efficient vehicle-level

operations while leveraging the ESP32’s advanced

connectivity capabilities for emergency

communication. The architecture is divided into three

primary components: vehicle hardware, vehicle-to-

vehicle (V2V) communication, and the centralized

communication hub.

Vehicle Hardware: Each vehicle is equipped with

Arduino UNO to control essential sensors, actuators,

and communication modules. The hardware

components include:

UV Sensors: Positioned at the front and rear of the

vehicle to measure distances from nearby obstacles or

vehicles. These sensors enable real-time hazard

ADXL345 Accelerometers: Detect sudden

acceleration changes along three axes (X, Y, Z) to

identify potential collisions or abrupt movements.

LDR Sensors: Monitor ambient light levels to

dynamically adjust the vehicle’s headlight in- tensity,

reducing glare during nighttime driving.

Arduino UNO: Functions as the vehicle’s primary

processing unit, handling sensor data acquisition,

processing, and triggering actions like braking or

V2V communication.

Motor Drivers and Relays: Enable control over the

vehicle’s movement and safety mechanisms, such as

braking and headlight adjustments.

Vehicle-to-Vehicle Communication: Zigbee modules

connected to the Arduino UNO facilitate real-time

communication between vehicles. This V2V

communication ensures prompt data sharing for

critical scenarios, such as Obstacle detection alerts to

nearby vehicles. Notifications of sudden deceleration

or impact. Warnings about low fuel levels to prevent

breakdowns. The Arduino UNO collects and

processes sensor data and transmits these alerts via

Zigbee modules to nearby vehicles, ensuring a rapid

and coordinated response.

Centralized Communication Hub: The central

communication hub, using ESP32, plays an important

role in managing the emergency notification and

more widespread communication. Equipped with

WiFi connectivity, the ESP32 connects to the internet

for sending messages to the cloud-based services or

the stakeholders. It integrates with a Telegram bot for

forwarding the critical alerts such as accident reports

and low-fuel warnings to the preconfigured contacts

including the authorities and owners of the vehicles.

It processes incoming Zigbee messages that it

translates into actionable alerts for efficient

dissemination.

The hybrid system workflow starts with hazard

detection by Arduino UNO-based vehicles using UV

sensors and accelerometers, which sends safety

signals such as auto-braking and alerting other

vehicles in the neighborhood through Zigbee. These

alerts are received by ESP32 hub and broadcast to

concerned parties in real-time, which makes this

hybrid design an economical option of Arduino

UNO-based design with ESP32-based connectivity

for hazard detection, which can be scaled in the

future. It addresses gaps in transportation safety,

paving the way for smarter, safer roads.

4 METHODOLOGY

Sensor Integration: UV sensors are used for front

object detection and blind spot monitoring. ADXL

accelerometer sensor detects sudden changes in

vehicle movement, identifying accidents. The LDR

sensor detects high beams from on- coming vehicles

for automatic headlight adjustment message

transmission.

Data Processing: The Arduino UNO microcontroller

collects and procedures information from all of the

sensors in actual- time.

Collision Detection: When a potential collision or

accident is detected, the system immediately

identifies the hazard using UV and accelerometer

data.

Vehicle-to-Vehicle Communication: Zigbee

communication is utilized to send alerts to nearby

vehicles, warning them of potential danger.

Automatic Braking: Upon receiving the collision

Chain Accident Prevention by Applying Automatic Braking via Vehicle to Vehicle Communication

159

alert, nearby vehicles automatically apply their brakes

to prevent a chain accident.

Headlight Adjustment: If an LDR sensor detects a

high beam from oncoming traffic, the vehicle will

receive a message to switch to a low beam to improve

visibility.

Emergency Communication: A central hub collects

accident data and sends emergency alerts to pre-

configured contacts via Telegram for quick

assistance.

Real-Time Monitoring: The “Chain Accident

Prevention by Applying Automatic Brake via

Vehicle-to-Vehicle Communication” project utilizes

IoT-based technology to improve road safety and

prevent chain collisions. Here, the system uses UV

sensors for real-time object detection along with blind

spot monitoring wherein the system triggers

immediate responses if obstacles are detected to be

within critical range. An ADXL accelerometer finds

sudden deceleration or im- pacts, prompting accident

alerts. Zigbee modules enable low- latency vehicle-

to-vehicle communication, allowing surrounding

vehicles to activate automatic braking mechanisms,

reducing re- action times and mitigating collisions.

To address night-time driving hazards, LDR sensors

detect and with the help of Zigbee send messages to

adjust headlights from high to low beams, reducing

glare and enhancing visibility. The system also

includes a centralized hub that relays emergency

notifications with location details via Telegram,

ensuring swift response from emergency services.

Each car carries an LCD which will be displaying

real-time sensor data, alerts, and system statuses. This

means there will be a greater improvement in safety,

with reduced accidents and a more efficient driving

environment.

Below are the core components and

functionalities of the proposed system:

Front Object Detection and Blind Spot Monitoring:

The system utilizes UV sensors to monitor the

distance between the vehicle and obstacles in front or

within blind spots. This ensures continuous tracking

of close by items, alerting the machine if an

impediment is within a predefined safe range.

Accident Detection System: ADXL accelerometer is

used to come across unexpected changes in

acceleration, including those as a result of a collision

or difficult braking. If the gadget detects values

exceeding the protection threshold, it acknowledges

the occasion as a potential accident and triggers alerts.

Vehicle-to-Vehicle Communication (V2V): Zig- bee

modules facilitate verbal exchange between vehicles

within a defined range. When a car detects a coming

near collision or a coincidence, it sends an alert to

close by motors. These cars routinely have interaction

with their braking structures upon receiving the alert,

preventing a chain reaction.

Automatic Braking Mechanism: Upon receiving a

collision alert, the machine automatically activates

the vehicle’s braking gadget. This function removes

human response delays, imparting a faster reaction to

keep away from or mitigate collisions.

High Beam Detection: The system incorporates an

LDR sensor to detect high beam headlights from

oncoming vehicles. When detected, the system

automatically informs the other vehicle to switch to a

low beam, enhancing visibility and reducing glare for

all drivers on the road.

Centralized Hub and Emergency Communication:

The proposed system includes a centralized hub

connected to all vehicles via the Zigbee network. In

the event of an accident, the hub receives alerts and

sends emergency notifications to pre- configured

contacts through the Telegram plat- form. This

ensures timely assistance and quick response from

emergency services.

Real Time Monitoring and Display: LCD is in- stalled

in the vehicle to provide real-time feed- back to the

driver. The display shows critical information,

including the distance to obstacles, system alerts, and

the status of vehicle-to-vehicle communication. This

helps drivers stay aware of their surroundings and the

system’s actions.

System Workflow is discussed below

Continuous Monitoring: UV sensors and the ADXL

accelerometer continuously monitor the vehicle’s

environment and movement.

Hazard Detection: If an obstacle or sudden decel-

eration is detected, the system evaluates the risk of a

collision.

Alert Transmission: The vehicle sends alerts to

nearby vehicles via Zigbee.

Automatic Braking: On receiving an alert, the nearby

vehicles automatically apply brakes to pre- vent a

chain collision.

High Beam Adjustment: LDR sensors detect on-

coming high beams and communicate to switch

headlights to low beam to the other vehicle.

Emergency Alerts: In case of an accident, the system

communicates with the hub, which sends

notifications through Telegram for emergency

response.

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5 BLOCK DIAGRAM

Figure 5.1: Block diagram of the proposed System.

Figure 5.2: ESP32 interface.

6 RESULTS OF THE RESEARCH

Reliable Hazard Detection: The system was found to

be working well in detecting obstacles and hazards

through UV sensors while monitoring the

surroundings of a vehicle in Real Time.

Accelerometers (ADXL345) successfully detected

jerks or collisions, thus enabling alerts and safety

mechanisms accordingly.

Figure 6.1: Prototype of the system.

Figure 6.2: Result 1.

Improved Communication: Communication modules

based on Zigbee ensured low-latency trans- mission

of data between vehicles efficiently, thus allowing for

swift transmission of alerts about potential hazards,

accidents, or sudden braking

Figure 6.3: Result 2.

Automatic Braking Implementation: Vehicles with

the system deployed automatically applied brakes in

case of collision alerts, preventing chain reaction

accidents.

Centralized Emergency Response: The ESP32-based

centralized hub relayed emergency notifications to

preconfigured contacts via Telegram, thus ensuring

timely responses from authorities and other

stakeholders.

Figure 6.4: HUB.

Scalability and Cost Efficiency of the System: The

use of low-cost hardware, such as Arduino UNO and

Zigbee modules, made the system cost- effective

enough for mass adoption without affecting

reliability. The system would allow easy integration

of additional sensors or technologies in the future

Chain Accident Prevention by Applying Automatic Braking via Vehicle to Vehicle Communication

161

because of its modular structure.

Real-time monitoring and feedback: The inclusion of

an LCD in the vehicle allowed the drivers to

continuously monitor distance to obstacles, status of

the system, and alerts, thereby enhancing situational

awareness and decision-making.

Figure 6.5: Result 3.

Overall Safety Enhancement: In the initial tests, a

significant reduction in the likelihood of rear end and

chain collisions was observed, whereas response in

emergency situations was faster thus indicating the

scope of saving lives on the roads is vast.

7 CONCLUSION

In conclusion, it’s the project “Vehicle-to-Vehicle

Communication” an ambitious development aimed

toward improving road safety through reducing a

common danger found in present-day traffic, namely

the chain collision. Advanced technologies of IoT in

the system allow real-time communication from one

vehicle to another which enables immediate

automated response. UV sensors perceive obstacles

or blind spots; the accelerometer ADXL picks up

rapid deceleration and impacts hence, prompt

detection of accidents is ensured. Zigbee

communication ensures smooth data transfer,

automated braking to minimize human factors, and

prevention of multi-car collisions. The LDR sensor

enhances night safety from glare by informing the

driver about adjusting headlights. It is a centralized

hub that ensures proper emergency response through

alerts on Telegram, making sure timely help reaches

the affected area. An LCD keeps the driver updated

about the status of the system and the environment,

ensuring an informed decision. This will be an all-

inclusive approach by combining automation,

communication, and safety technologies for safer

roads. Scalable and adaptable, it can be used in smart

city infrastructures and future

development like

machine learning.

ACKNOWLEDGEMENT

We express our sincere gratitude to Dayananda

Sagar College of Engineering for providing the

necessary resources and infrastructure to carry out

this research. We are deeply thankful to the

Department of Electronics and Communication

Engineering for their unwavering support and

guidance throughout the project. Our heartfelt thanks

extend to our project guide, Prof. Sowmya P., whose

expertise, encouragement, and constructive feedback

were instrumental in shaping this project and

overcoming challenges. We also acknowledge the

assistance provided by the college in testing and

validating the system’s components. Finally, we are

grateful to our peers and collaborators, whose

valuable insights and encouragement inspired us to

achieve the objectives of this research.

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