{"id":20456015,"url":"https://github.com/vm32/gr-project","last_synced_at":"2026-03-08T11:37:53.475Z","repository":{"id":157288256,"uuid":"596476110","full_name":"vm32/GR-Project","owner":"vm32","description":"Using Drones and Advanced Technologies for Automatic Reporting of Vehicular Accidents","archived":false,"fork":false,"pushed_at":"2023-12-12T18:58:43.000Z","size":106,"stargazers_count":7,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-15T23:30:29.714Z","etag":null,"topics":["ai","drone","sensor"],"latest_commit_sha":null,"homepage":"","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/vm32.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-02-02T09:10:31.000Z","updated_at":"2024-04-06T11:21:05.000Z","dependencies_parsed_at":null,"dependency_job_id":"d944d031-10f4-4532-ae02-e9bf983f827d","html_url":"https://github.com/vm32/GR-Project","commit_stats":null,"previous_names":["vm32/gr-project"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vm32%2FGR-Project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vm32%2FGR-Project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vm32%2FGR-Project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vm32%2FGR-Project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vm32","download_url":"https://codeload.github.com/vm32/GR-Project/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242007674,"owners_count":20056773,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","drone","sensor"],"created_at":"2024-11-15T11:20:49.340Z","updated_at":"2026-03-08T11:37:53.463Z","avatar_url":"https://github.com/vm32.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Automated Accident Reporting with Drones and Advanced Technologies\n\n## 🚨 Overview\nThis 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.\n\nThe solution can quickly survey accident scenes to capture critical information including:\n- Precise vehicle locations\n- Road conditions and environmental factors\n- Accident severity assessment\n- Real-time data transmission to first responders\n\n## 🔄 System Architecture\n![Block Diagram of the Project](https://img001.prntscr.com/file/img001/qhsreyFZTXC3yQcBY01kug.png)\n\n## 🛠️ Hardware Components\n### Vehicle-Side Hardware\n- Arduino Uno (R3)\n- MPU6050 (Accelerometer and Gyroscope)\n- Temperature Sensor (integrated in MPU6050)\n- GPS/GSM Module\n\n### Drone Hardware\n- Quadcopter Frame\n- Pixhawk Flight Controller\n- Remote Controller\n- EWRF Drone Camera\n- GPS Antenna\n- Radio Telemetry\n- Brushless Motors (4x)\n- 40A Brushless ESCs (4x)\n- 4 Pairs Quadcopters Multirotor Propellers\n- RC Power Distribution Board\n- 4S LiPo Battery (5000mAh)\n- Smart Balance LiPo Charger\n\n## 💻 Software Requirements\n- **Arduino Platform**: For sensor programming and integration\n- **Tinker CAD**: For circuit simulation and testing\n- **Microsoft Visual Studio Code**: For software development\n- **Mission Planner**: For drone flight planning and simulation\n- **QGroundControl**: For drone calibration and configuration\n\n## 🔍 Accident Detection Implementation\n\n### 1. Accelerometer-Based Detection\nThe system monitors sudden changes in acceleration to detect potential collisions:\n\n```cpp\nif (a.acceleration.x \u003e 0.06) { \n    digitalWrite(4, HIGH);\n    digitalWrite(2, LOW); \n    delay(100);\n    Serial.print(\"Accident detect on acceleration : \"); \n    Serial.print(a.acceleration.x);                    \n    digitalWrite(4, LOW);\n    digitalWrite(2, LOW);  \n    delay(100);\n}\n```\n\n![Accelerometer Detection](https://img001.prntscr.com/file/img001/8LoVwwFRTzOrRGZwK_exgg.png)\n\n### 2. Gyroscope-Based Detection\nThe system detects unusual rotational movements that may indicate rollovers or impacts:\n\n```cpp\nif (g.gyro.y \u003e 0.45) { \n    digitalWrite(4, HIGH);\n    digitalWrite(2, LOW); \n    delay(100);\n    Serial.print(\"Accident detect on gyro  : \"); \n    Serial.print(g.gyro.y);                    \n    digitalWrite(4, LOW);\n    digitalWrite(2, LOW);  \n    delay(100);\n}\n```\n\n![Gyroscope Detection](https://img001.prntscr.com/file/img001/GRhQyPLESw6kxD3F0_3fJQ.png)\n\n### 3. Temperature Monitoring\nThe system monitors temperature spikes that could indicate fire or engine damage:\n\n```cpp\nif (temp.temperature \u003e 45) { \n    digitalWrite(4, HIGH);\n    digitalWrite(2, LOW); \n    delay(100);\n    Serial.print(\"temperature is HIGH   : \"); \n    Serial.print(temp.temperature);                    \n    digitalWrite(4, LOW);\n    digitalWrite(2, LOW);  \n    delay(100);\n}\n```\n\n![Temperature Monitoring](https://img001.prntscr.com/file/img001/7QWsx3l3QdeilpaXLUOuYg.png)\n\n## ☁️ Cloud Implementation\nOur system includes a complete web-based monitoring and reporting platform.\n\n### Front-end Interface (GitHub Pages)\n![Front-end Interface](https://img001.prntscr.com/file/img001/ShpI3edLSVKgu5-Ut5p0sg.png)\n\n### Back-end System (GitHub Pages)\n![Back-end System](https://img001.prntscr.com/file/img001/2oWur0Y-SKOqoCYATWw5TQ.png)\n\n## 🌟 Real-World Benefits and Impact\n\n### Faster Emergency Response\n- **Real-time Detection**: Automated alerts sent immediately upon accident detection\n- **Enhanced Situational Awareness**: Drone footage provides responders with visual understanding before arrival\n- **Optimized Resource Deployment**: More accurate accident severity assessment allows for appropriate resource allocation\n\n### Improved Accident Reporting and Analysis\n- **Comprehensive Data Collection**: Sensor data and aerial imagery provide unprecedented detail\n- **Objective Evidence**: Reduces reliance on witness statements and provides factual documentation\n- **Temporal Analysis**: System captures pre- and post-accident data for thorough investigation\n\n### Socioeconomic Benefits\n- **Potential Life Saving**: Reduced response times can significantly impact survival rates\n- **Insurance Processing**: Faster, more accurate claims processing\n- **Urban Planning**: Data can identify accident-prone areas for infrastructure improvements\n- **Cost Reduction**: More efficient emergency services utilization and reduced traffic congestion from accidents\n\n## 🚀 Future Enhancements\n- Integration with traffic management systems\n- Machine learning algorithms for predictive accident prevention\n- Enhanced computer vision for automated damage assessment\n- Integration with emergency services dispatch systems\n- Expanded sensor suite for more comprehensive data collection\n\n---\n\nThis 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.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvm32%2Fgr-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvm32%2Fgr-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvm32%2Fgr-project/lists"}