REAR COLLISION AVOIDANCE SYSTEM USING MACHINE LEARNING INTEGRATED WITH ARDUINO - Abstract

Aug 2, 2026

The rear collision avoidance system using machine learning integrated with Arduino is designed to enhance vehicle safety by preventing rear-end collisions through real-time detection and alert mechanisms. It analyzes sensor data from ultrasonic sensors mounted at the vehicle rear to identify approaching vehicles or obstacles, including their distance and speed. Machine learning models predict potential collisions based on diverse driving scenarios, while Arduino serves as the core controller for data collection and decision execution. Alerts such as buzzer alarms and LCD notifications inform drivers, and optional wireless communication enables forwarding data to a central monitoring system for further analysis and improvement. The approach reduces rear-end crashes, minimizes human error, and offers a cost-effective integration path for existing vehicle models.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Abstract

Introduction

Overview of Road Safety Concerns

Importance of Collision Avoidance Systems

Role of Machine Learning in Enhancing Safety

Integration of Arduino in Collision Avoidance Systems

Literature Survey

20(2): S2: 278-283, 2025 [www.thebioscan.com](www.thebioscan.com) REAR COLLISION AVOIDANCE SYSTEM USING MACHINE LEARNING INTEGRATED WITH ARDUINO 1) Dr S Sri Gowri, Professor, ECE department, SRK INSTITUTE OF TECHNOLOGY, Enikepadu, Vijayawada 521108 Mail Id: [srkecehod@gmail.com](srkecehod@gmail.com) [Contact:](Contact: 7093322366)[ 7093322366](Contact: 7093322366) 2) Mr. V. Uma Manikanta, PG student , ECE department, Newtons Institute of Engineering, Macherla Mail [ID:](ID: vakkalagaddaumamanikanta@gmail.com)[ ](ID: vakkalagaddaumamanikanta@gmail.com)[vakkalagaddaumamanikanta@gmail.com](ID: vakkalagaddaumamanikanta@gmail.com) [Contact:](Contact: 9182365689)[ 9182365689](Contact: 9182365689) DOI: 10.63001/tbs.2025.v20.i02.S2.pp278-283 Received on: 01-03-2025 Accepted on: 07-04-2025 Published on 11-05-2025 ABSTRACT The rear collision avoidance system using machine learning integrated with Arduino is designed to enhance vehicle safety by preventing rear-end collisions through real-time detection and alert mechanisms. This system utilizes machine learning algorithms to analyze sensor data from ultrasonic mounted at the rear of the vehicle, enabling it to detect the presence, speed, and distance of approaching vehicles or obstacles. By leveraging machine learning models trained on diverse driving scenarios, the system can predict potential collisions and trigger appropriate warnings. The Arduino microcontroller serves as the core processing unit, collecting sensor data and executing the trained model’s decision-making process. The system is further equipped with an alerting mechanism, such as buzzer alarms and LCD, to notify drivers of imminent threats. Additionally, the integration of wireless communication allows data transmission to a central monitoring system for further analysis and improvements. The implementation of such an intelligent rear collision avoidance system significantly enhances road safety by reducing the likelihood of rear-end crashes, minimizing human errors, and offering a cost-effective solution that can be easily incorporated into existing vehicle models. INTRODUCTION 1.1 Overview of Road Safety Concerns Road safety remains one of the most critical aspects of modern transportation systems, with millions of accidents occurring worldwide due to human errors, poor visibility, and lack of proper alert mechanisms. Among various types of road accidents, rearend collisions are one of the most frequent and hazardous. These collisions occur when a vehicle crashes into the one in front of it due to misjudgement, delayed reaction time, or distraction. With the advancement of technology, automotive safety systems have evolved to mitigate such risks, and intelligent solutions have emerged to prevent collisions and ensure driver safety. 1.2 Importance of Collision Avoidance Systems The introduction of collision avoidance systems has significantly contributed to reducing the number of accidents on roads. These systems are designed to detect obstacles, warn drivers, and in some cases, automatically take corrective actions to prevent collisions. Traditional collision avoidance technologies rely on simple sensors that measure distances and trigger alarms when an object is too close. However, with the rise of artificial intelligence and machine learning, collision detection systems have become more sophisticated and capable of making real-time decisions. A machine learning-based rear collision avoidance system can analyze complex scenarios, recognize patterns, and provide timely alerts to avoid accidents effectively. 1.3 Role of Machine Learning in Enhancing Safety Machine learning has revolutionized various industries, and its application in automotive safety is rapidly growing. By training models on a vast dataset of driving scenarios, machine learning algorithms can predict potential hazards with higher accuracy compared to conventional sensor-based systems. These models can d

FAQs

How does the system detect vehicles or obstacles behind the car?

It uses ultrasonic sensors mounted at the rear to capture proximity-related sensor data, which are then processed by the machine learning model to identify the presence and relative conditions of approaching entities.

What role does Arduino play in the proposed rear collision avoidance system?

Arduino acts as the core processing unit, collecting sensor inputs and running the decision-making process produced by the trained machine learning model to control warnings and actions.

How are drivers alerted when a potential rear-end collision is predicted?

The system triggers alert mechanisms such as buzzer alarms and an LCD display to notify the driver of imminent threats.

Aug 2, 2026

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