Global Planning Method for Visiting Roads with Parking Spaces in Priority using Rural Postman Problem

Abstract

Global planning is one of the key components in autonomous driving. At a parking lot, the purpose of the global planning is to find an empty parking space. Therefore, the autonomous vehicle is to visit roads with parking spaces until the empty space is found. In this paper, to achieve the purpose, the global planning is formulated as the Rural Postman Problem (RPP) and a genetic algorithm (GA) is used to solve the RPP. However, GA produces a plan to randomly visit roads, which may not be efficient. Therefore, in this paper, we propose a cost function considering the parking availability, order of visiting, length of each road segment, and forward-backward switching of a vehicle. The cost function enables generating a plan by prioritizing visiting the roads with parking spaces. Experimental results show that solving the RPP with the proposed cost function generates a more efficient plan than that produced by solving the RPP without the cost function.