AI based emergency vehicle priority system

Abstract

Emergency vehicle priority (EVP) systems are the need of the hour to reduce the transit time of emergency vehicles in cities. As these cities are major hubs of economic activity they are one of the most densely populated cities in the world. Due to numerous such issues, ambulances are not able to reach patients and hospitals on time. In this paper, we propose a system that detects an ambulance accurately and helps set up a makeshift emergency lane on the routes to be taken by it. The system relies on a neural network-based siren classifier to detect the ambulance using audio processing. The overall accuracy of the siren classifier was 97.2 %. After the ambulance is detected this information is then passed onto a network of Internet of Things (IoT) devices that activate visual indicators on the routes to be taken by the ambulance. On activating the visual indicators the traffic on those roads can start making a temporary emergency lane. The system uses a GPS-based mobile app to get route information of the ambulance. The network of IoT devices consists of a host device and station/node devices in a chain-like connection, where all devices are communicating via local WiFi networks. The host receives information about the ambulance from the neural network and the mobile app. The host then sends this information down the chain to other node devices. Through our proposed system we hope that the transit time of ambulances is reduced and hence accident victims, heart attack patients, etc can get medical attention faster.