Dynamic Path Planning Approaches based on Artificial Intelligence and Machine Learning

Abstract

Prediction plays an important role where we are trying to generate probable values
for an unknown variable for each record in the new data, allowing the model builder to
identify what that value will most likely be and where that value will be useful. Machine
learning model predictions allow businesses to make highly accurate guesses as to the likely
outcomes of a question based on historical data, which can be about all kinds of things –
customer churn likelihood, possible fraudulent activity, and more. In this paper we are trying
to present Path prediction methods and algorithms available and compare them. In this paper
we are considering the Path prediction issues and problems during taxi and cab driving. In
systems today, many drivers face issues while serving client requests during the drive
because of many unpredictable events that. Similarly, although a person may shop on
different days or at different times, they will often vision analyse the happen daily on the
roads. In this paper, we try to find out more improved methods for alternate Path prediction
and switching Paths due incidents and other unexpected events based on obstacle free Path
prediction also allowing the reduction of delays. This paper reviews the literature regarding
various machine learning models, algorithms & approaches focusing on Path prediction.We
start by analyzing road network data collection algorithms and their efficiency.In the later
part of the paper, wetry to talk about metrics and parameters commonly used to evaluate
prediction, in order to compare the different approaches. We list, detail and compare existing
algorithms that provide Path predictions. This research leads to an understanding of
advantages, disadvantages and trade-offs of the methods studied and will surely provide
useful information for future development.