Its Applications Using Embedded Systems Vehicle to Infrastructure Communication EVM

Abstract

Traffic around the world is increasing endlessly and will cause terrible traffic jams at intersections. Therefore, blocking ambulances, police cars, fire trucks, etc., from getting stuck in traffic can lead to property loss and precious lives. In addition, there are many situations where traditional solutions are not very effective, for example, when processing large amounts of data collected from automotive sensors and network devices. Several Artificial Intelligence (AI) based methods have been applied in various areas related to the transport environment to overcome these problems. Previously, the shortest path-based technique was used to determine the best path from source to destination for emergency vehicles. But mostly, more congestion can happen in the route with the shortest distance between source and destination. In urban areas, most people use roads that have the shortest distance from source to destination. This is one of the significant drawbacks of shortest distance-based path prediction techniques. newlineAlso, nonlinear route parameters may affect the transition time of an emergency vehicle. Some previous works used only static route parameters such as distance, road width, road type, and other related parameters to predict the best path for emergency vehicles. This work proposed an AI-based approach to improving the performance of Intelligent Transport Systems (ITS), especially for emergency vehicles, to predict their best path to reduce the transition time. Various linear and nonlinear route parameters are considered to improve the accuracy of the system. The critical parameters such as distance, traffic density, road width, number of curves, slope up and down, average vehicle per day, and weather conditions are considered in this work to perform the prediction process. newlineThis work uses multiple combinations of AI techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Extreme Learning Machine (ELM) to perform path prediction with high