Development of an AI and IoT based system for accident prevention, detection and real time emergency response: Case study: Rwanda

Abstract

Road incidents caused by driver distractions (such as phone usage), drunkenness, and excessive speed constitute significant dangers to public safety in Rwanda. A comprehensive study of the design and implementation of an AI and IoT-based system for accident detection, prevention, and enhanced emergency response is presented in this thesis as one of the solutions to the problem. Using Raspberry Pi microcontroller as the central server, the system incorporates several sensors and actuators connected to ESP8266, including GPS Gn801 GPS Glonass dual mode for speed detection and car localization, MQ3 alcohol gas sensor SEN42, R16 for alcohol detection, ADXL335-3 Axis compass accelerometer GY-6l for car movement changes. In addition, a Raspberry Pi 4Bl3B 5MP fisheye night vision focal camera is integrated into the system to detect driver distractions such as phone usage while driving. Sophisticated machine learning and artificial intelligence (AI) algorithms, with a specific focus on Convolutional Neural Networks (CNNs), have been utilised to identify instances of driver distraction and facilitate real-time monitoring and alert systems. By leveraging on cloud computing to store and analyse data, the system guarantees efficient and impactful correspondence with emergency response teams. Our objective is to contribute to the domain of road safety by developing an efficient solution for emergency response and accident prevention in Rwanda. Our research emphasizes the importance of utilizing technology to address critical issues related to road safety. The study's findings demonstrate the accuracy of the integrated IoT sensors in accurately detecting accidents, speed, monitoring alcohol consumption, and identifying distractions, specifically phone usage. A remarkable 86-100% accuracy is attained by Custom Convolutional Neural Networks (CNNs) when it comes to identifying driver distractions. The combination of edge and cloud computing guarantees timely and effective communication with emergency response teams. This study highlights the critical role that technology plays in improving road safety by proposing a technologically advanced solution for emergency response and accident prevention in Rwanda. The capabilities of the system to monitor in realtime and its efficacy in accident detection and prevention demonstrate its potential to make a substantial impact on the field of road safety.