https://github.com/vm32/gr-project
Using Drones and Advanced Technologies for Automatic Reporting of Vehicular Accidents
https://github.com/vm32/gr-project
ai drone sensor
Last synced: 4 months ago
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Using Drones and Advanced Technologies for Automatic Reporting of Vehicular Accidents
- Host: GitHub
- URL: https://github.com/vm32/gr-project
- Owner: vm32
- Created: 2023-02-02T09:10:31.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-12-12T18:58:43.000Z (over 2 years ago)
- Last Synced: 2025-01-15T23:30:29.714Z (over 1 year ago)
- Topics: ai, drone, sensor
- Language: HTML
- Homepage:
- Size: 104 KB
- Stars: 7
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Automated Accident Reporting with Drones and Advanced Technologies
## 🚨 Overview
This cutting-edge solution revolutionizes vehicular accident reporting by leveraging drones and advanced sensor technologies. Our system provides rapid, accurate data collection at accident scenes, enabling faster emergency response and potentially saving lives.
The solution can quickly survey accident scenes to capture critical information including:
- Precise vehicle locations
- Road conditions and environmental factors
- Accident severity assessment
- Real-time data transmission to first responders
## 🔄 System Architecture
## 🛠️ Hardware Components
### Vehicle-Side Hardware
- Arduino Uno (R3)
- MPU6050 (Accelerometer and Gyroscope)
- Temperature Sensor (integrated in MPU6050)
- GPS/GSM Module
### Drone Hardware
- Quadcopter Frame
- Pixhawk Flight Controller
- Remote Controller
- EWRF Drone Camera
- GPS Antenna
- Radio Telemetry
- Brushless Motors (4x)
- 40A Brushless ESCs (4x)
- 4 Pairs Quadcopters Multirotor Propellers
- RC Power Distribution Board
- 4S LiPo Battery (5000mAh)
- Smart Balance LiPo Charger
## 💻 Software Requirements
- **Arduino Platform**: For sensor programming and integration
- **Tinker CAD**: For circuit simulation and testing
- **Microsoft Visual Studio Code**: For software development
- **Mission Planner**: For drone flight planning and simulation
- **QGroundControl**: For drone calibration and configuration
## 🔍 Accident Detection Implementation
### 1. Accelerometer-Based Detection
The system monitors sudden changes in acceleration to detect potential collisions:
```cpp
if (a.acceleration.x > 0.06) {
digitalWrite(4, HIGH);
digitalWrite(2, LOW);
delay(100);
Serial.print("Accident detect on acceleration : ");
Serial.print(a.acceleration.x);
digitalWrite(4, LOW);
digitalWrite(2, LOW);
delay(100);
}
```
### 2. Gyroscope-Based Detection
The system detects unusual rotational movements that may indicate rollovers or impacts:
```cpp
if (g.gyro.y > 0.45) {
digitalWrite(4, HIGH);
digitalWrite(2, LOW);
delay(100);
Serial.print("Accident detect on gyro : ");
Serial.print(g.gyro.y);
digitalWrite(4, LOW);
digitalWrite(2, LOW);
delay(100);
}
```
### 3. Temperature Monitoring
The system monitors temperature spikes that could indicate fire or engine damage:
```cpp
if (temp.temperature > 45) {
digitalWrite(4, HIGH);
digitalWrite(2, LOW);
delay(100);
Serial.print("temperature is HIGH : ");
Serial.print(temp.temperature);
digitalWrite(4, LOW);
digitalWrite(2, LOW);
delay(100);
}
```
## ☁️ Cloud Implementation
Our system includes a complete web-based monitoring and reporting platform.
### Front-end Interface (GitHub Pages)
### Back-end System (GitHub Pages)
## 🌟 Real-World Benefits and Impact
### Faster Emergency Response
- **Real-time Detection**: Automated alerts sent immediately upon accident detection
- **Enhanced Situational Awareness**: Drone footage provides responders with visual understanding before arrival
- **Optimized Resource Deployment**: More accurate accident severity assessment allows for appropriate resource allocation
### Improved Accident Reporting and Analysis
- **Comprehensive Data Collection**: Sensor data and aerial imagery provide unprecedented detail
- **Objective Evidence**: Reduces reliance on witness statements and provides factual documentation
- **Temporal Analysis**: System captures pre- and post-accident data for thorough investigation
### Socioeconomic Benefits
- **Potential Life Saving**: Reduced response times can significantly impact survival rates
- **Insurance Processing**: Faster, more accurate claims processing
- **Urban Planning**: Data can identify accident-prone areas for infrastructure improvements
- **Cost Reduction**: More efficient emergency services utilization and reduced traffic congestion from accidents
## 🚀 Future Enhancements
- Integration with traffic management systems
- Machine learning algorithms for predictive accident prevention
- Enhanced computer vision for automated damage assessment
- Integration with emergency services dispatch systems
- Expanded sensor suite for more comprehensive data collection
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This project represents a significant advancement in road safety technology, combining IoT sensors, drone capabilities, and cloud computing to create a comprehensive accident detection and reporting system.