Collaborative humanless model for automatic pothole detection and driver notification. (8th May 2020)

• Record Type: Journal Article
• Authors: Lopes, Thiago Roberto Lima; Dias, Lucas Pfeiffer Salomão; Costa, Cristiano André Da; Nardin, Igor Fontana De; Righi, Rodrigo Da Rosa
• Is Part Of: International journal of computational science and engineering. Volume 22:Number 2/3(2020)
• Page Start: 280
• Page End: 296
• Publication Date: 2020-05-08
• Subjects: pothole detection -- machine-to-machine collaboration -- collaborative systems -- safe driving -- CoMDAP; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

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.