Open AccessCollision Avoidance by Identifying Risks for Detected Objects in Autonomous Vehicles
Author(s) -
Haidr Ghasn Hasn,
Manjoor Ali
Publication year - 2021
Publication title -
embedded selforganising systems
Language(s) - English
Resource type - Journals
ISSN - 1869-5213
DOI - 10.14464/ess.v7i1.472
Subject(s) - object (grammar) , computer vision , computer science , artificial intelligence , collision avoidance , plan (archaeology) , collision , object detection , monocular , monocular vision , computer security , geography , pattern recognition (psychology) , archaeology
We propose a system which will detect objects onour roads, estimate the distance of these object from the cameraand alert the driver if this distance is equal or less than thethreshold value(02meters),and assist the driver and alert him assoon as possible in order for him to take appropriate actions assoon as possible which can avoid any collision or significantlyreduce it. We plan to use state of the arts object detection modelslike YOLO to identify the target object classes and use depthmaps from monocular camera to be give an accurate estimate ofthe distance of the detected object from the camera. one majorrequirement of this system is the real-time behaviour and a highaccuracy for the detected and estimated distance, A secondrequirement is to make the system cheap and easy useablecomparatively to the other existing methods. That is why wedecided to use monocular camera images and depth maps whichmakes the solution cheap and innovative. This project(prototype) provide room for bigger and more complete projectwhich will contribute to the creation of tool which can save livesand improve security on our roads