[AfA2026-PhysicalAI] – Accident Response System

40 colored male-female jumper wires

Power: USB-C Cable

Python 3

Modulino™ Knob

USB-C to HDMI multiport adapter with ethernet and USB hub

Modulino™ Movement

Arduino IDE

a laptop or a computer to test

40 colored female-female jumper wires

Arduino® UNO™ Q 2GB

Modulino™ Distance

SMS Messaging API

Twilio API for WhatsApp

Modulino™ Thermo

Arduino App Lab

webcam

Modulino™ Latch Relay

OpenCV

FTCBlock IIC I2C TWI Serial 2004 20x4 LCD Module Shield with IIC/I2C Serial Interface Adapter Module for Arduino Uno Mega2560

[HB] Micro Servo SG90

Modulino™ Buzzer

<html><body><figure><img src="https://projects.arduinocontent.cc/d41f378f-7d02-4cef-8cc2-21bb03cced86.png"/><figcaption></figcaption></figure>Road accidents remain one of the leading causes of injury and fatalities worldwide, often aggravated by delayed emergency response, driver fatigue, mechanical overheating, and insufficient real-time monitoring. To address these critical issues, this project presents an Intelligent Accident Response and Prevention System built using Arduino UNO Q, integrated sensors, AI-based computer vision, and cloud-based alert services. The system is designed to prevent accidents, detect hazardous conditions, and ensure rapid communication during emergencies, thereby significantly improving road safety.<h3>System Overview:</h3>The proposed system is a multi-layered safety solution that continuously monitors the vehicle’s surroundings, internal conditions, and driver alertness. It combines hardware-based sensing, edge-level control, and AI-powered decision-making, supported by real-time IoT communication. The system operates autonomously and reacts instantly to dangerous situations without requiring human intervention.1. Distance-Based Obstacle Detection and Collision Prevention:To prevent frontal collisions and detect sudden obstacles, a distance sensor is interfaced with the Arduino UNO Q. The sensor continuously measures the distance between the vehicle and nearby objects. When an object is detected within a predefined safety threshold, the system immediately triggers corrective action.The measured distance data is sent from the Arduino to a Python-based decision module via the Arduino Router Bridge. If the distance falls below the critical limit, a STOP command is issued, and a servo motor is activated to simulate braking or engine cutoff. This mechanism ensures rapid response to obstacles, reducing reaction time and minimizing collision risk.This module acts as the first line of defense, preventing accidents caused by sudden obstacles, poor visibility, or delayed driver response.2. Temperature Monitoring and Engine Safety Management:Vehicle overheating is a common cause of mechanical failure and fire-related accidents. To mitigate this risk, the system integrates a temperature sensor using the Modulino Thermo module. The sensor continuously monitors the engine or surrounding temperature in real time.The temperature data is transmitted to a Python monitoring service through the Arduino Bridge interface. If the temperature exceeds a predefined safe limit, the system categorizes the situation as critical and immediately sends an emergency SMS alert using the Twilio API. To avoid alert flooding, an intelligent cooldown mechanism is implemented, ensuring alerts are sent only when necessary.Additionally, the system periodically sends heartbeat messages at fixed intervals, confirming that the monitoring system is active and functioning normally. This dual-layer alerting mechanism enhances reliability and ensures continuous system awareness.3. AI-Based Driver Drowsiness Detection:Driver fatigue is one of the most underestimated yet dangerous contributors to road accidents. To tackle this, the system incorporates an AI-powered drowsiness detection module using computer vision.A live camera feed is processed using OpenCV and MediaPipe Face Mesh, enabling accurate detection of facial landmarks, particularly around the eyes. The system calculates the Eye Aspect Ratio (EAR) to determine whether the driver’s eyes remain closed beyond a safe duration.If prolonged eye closure is detected, indicating driver drowsiness or micro-sleep, the system immediately triggers an emergency alert via Twilio SMS, notifying a registered emergency contact. This real-time intervention significantly reduces the risk of fatigue-induced accidents.4. IoT Communication and Emergency Alert System:The system uses Twilio’s cloud communication API to ensure reliable and instant message delivery during critical situations. Alerts are triggered under the following conditions:<ol><li>Unsafe proximity detected by the distance sensor</li></ol><ol><li>Engine temperature exceeding safe thresholds</li></ol><ol><li>Driver drowsiness detected through AI analysis</li></ol>By integrating IoT communication, the system bridges the gap between on-vehicle intelligence and remote human intervention, enabling faster emergency response and informed decision-making.5. System Integration and Reliability:All modules are tightly integrated through a Python–Arduino hybrid architecture, where Arduino handles real-time sensor data acquisition and actuation, while Python performs higher-level decision-making and AI processing. This separation of concerns ensures efficiency, scalability, and maintainability.The system is designed to be modular, allowing easy expansion to include GPS tracking, accident impact detection, cloud dashboards, or automatic emergency service notification in future iterations.<h3>Applications and Impact:</h3><ol><li>Accident prevention in personal and commercial vehicles</li></ol><ol><li>Driver monitoring systems for long-distance transport</li></ol><ol><li>Smart vehicle safety enhancement</li></ol><ol><li>Emergency response automation</li></ol><ol><li>Research and prototyping for intelligent transportation systems</li></ol><h3>Conclusion:</h3>The Intelligent Accident Response and Prevention System demonstrates how embedded systems, artificial intelligence, and IoT technologies can be effectively combined to enhance vehicle safety. By proactively preventing accidents, monitoring critical parameters, and ensuring immediate emergency communication, the system provides a comprehensive and practical solution for modern road safety challenges.This project highlights a scalable, cost-effective, and real-world applicable approach to intelligent transportation safety, making it suitable for academic research, industrial prototyping, and smart mobility applications.</body></html>

Accident Detection Prevention System Using Arduino UNO-Q

[AfA2026-PhysicalAI] – Accident Response System

Transform your Arduino UNO Q into an intelligent accident response system that detects obstacles, monitors driver drowsiness and temperature, and automatically sends emergency alerts in real time.