<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abderaouf Menacer</style></author><author><style face="normal" font="default" size="100%">Larbi GUEZOULI</style></author><author><style face="normal" font="default" size="100%">Lyamine Guezouli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comparative Study of State-of-the-Art methods for vision-based Obstacle Detection</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Advances in Communication Technology,Computing and Engineering  (ICACTCE) - 2021</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">Forthcoming</style></year><pub-dates><date><style  face="normal" font="default" size="100%">24 March 2021</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Research into vision-based obstacle detection systems plays a fundamental role in developing autonomous vehicles and intelligent transportation systems. In fact, an intelligent vehicle should be ready to discover vehicles and possible obstacles along its route. This paper presents a comparative study of existing state-of-the-art methods for obstacle detection. However, there have been a large number of studies that thoroughly explored various types of state-of-the-art methods for obstacle detection. Here, this paper compares three methods in obstacle detection, namely the “Robust obstacle detection for ADAS using distortions of IPM of a monocular camera”, “Robust Obstacle Detection and Recognition for DAS”, and “Real-time Obstacle Detection Over Rails Using Deep CNN” and analyzes the obtained results.&amp;nbsp;</style></abstract></record></records></xml>