Collision Avoidance by Identifying Risks for Detected Objects in Autonomous Vehicles
Abstract: We propose a system which will detect objects on our roads, estimate the distance of these object from the camera and alert the driver if this distance is equal or less than the threshold value(02meters),and assist the driver and alert him as soon as possible in order for him to take appropriate actions as soon as possible which can avoid any collision or significantly reduce it. We plan to use state of the arts object detection models like YOLO to identify the target object classes and use depth maps from monocular camera to be give an accurate estimate of the distance of the detected object from the camera. one major requirement of this system is the real-time behaviour and a high accuracy for the detected and estimated distance, A second requirement is to make the system cheap and easy useable comparatively to the other existing methods. That is why we decided to use monocular camera images and depth maps which makes the solution cheap and innovative. This project (prototype) pr.... https://www.bibliothek.tu-chemnitz.de/ojs/index.php/cs/article/view/472
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Collision Avoidance by Identifying Risks for Detected Objects in Autonomous Vehicles ; volume:7 ; number:1 ; day:15 ; month:03 ; year:2021
Embedded selforganising systems ; 7, Heft 1 (15.03.2021)
- Urheber
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Hasn, Haidr Ghasn
Ali, Majed
- DOI
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10.14464/ess.v7i1.472
- URN
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urn:nbn:de:101:1-2023032815454310801256
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:59 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Hasn, Haidr Ghasn
- Ali, Majed
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