Title: Collaborative humanless model for automatic pothole detection and driver notification

Authors: Thiago Roberto Lima Lopes; Lucas Pfeiffer Salomão Dias; Cristiano André Da Costa; Igor Fontana De Nardin; Rodrigo Da Rosa Righi

Addresses: Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), São Leopoldo/RS, Brazil ' Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), São Leopoldo/RS, Brazil ' Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), São Leopoldo/RS, Brazil ' Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), São Leopoldo/RS, Brazil ' Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), São Leopoldo/RS, Brazil

Abstract: The bad conditions of roads characterised by potholes increase the occurrence of accidents, which sometimes also result in the loss of human lives. In this context, this article presents the collaborative model for detection and alert of potholes (CoMDAP), which provides a distributed framework that automatically collects, analyses and shares pothole and traffic data among users and drivers without any human interaction. Our differential idea consists of using particular hardware in the vehicles to automatically detect the potholes in the roads with better accuracy. The evaluation methodology first considers a prototype executed in simulated (a toy and in-home lanes) and real (a car in a particular road) scenarios in order to observe the accuracy of detecting the potholes. Furthermore, we implement an Android application that notifies the drivers as they approach a pothole. The results were encouraging, highlighting the benefits of using CoMDAP as a counterpart to enable smart cities.

Keywords: pothole detection; machine-to-machine collaboration; collaborative systems; safe driving; CoMDAP.

DOI: 10.1504/IJCSE.2020.107350

International Journal of Computational Science and Engineering, 2020 Vol.22 No.2/3, pp.280 - 296

Received: 07 Feb 2019
Accepted: 22 Jul 2019
Published online: 18 May 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article