Szabó, Máté András and Keszler, Anita and Tizedes, László (2025) Real-time detection of road hazards for autonomous vehicle systems. In: 2025 IEEE 6th International Conference on Image Processing, Applications and Systems (IPAS). Institute of Electrical and Electronics Engineers (IEEE), Piscataway (NJ), pp. 1-7. ISBN 9798331506520 10.1109/IPAS63548.2025.10924564

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Abstract

Automated detection of road hazards such as speed bumps, has become an important area of research due to its potential to improve road safety in autonomous driving. Various techniques have been introduced to detect these hazards using camera vision and artificial intelligence-based image processing methods. However, estimating their distance is still challenging. To address this problem and to satisfy the requirement for real-time on-board data processing, the proposed system has the following properties: (1) high-accuracy road hazard detection by analyzing mono-images and videos with a re-trained YOLO neural network; (2) precise distance measurement utilizing a LiDAR; and (3) efficient local data processing using ROS, implemented on an NVIDIA Jetson AGX Xavier. An important contribution of this paper is introducing multiple classes of road hazards when training the network, instead of only focusing on speed bumps and potholes. Furthermore we have analyzed different LiDAR technologies (standard rotating and non-repetitive circular scanning) to evaluate and compare their precision and to demonstrate that our method can be successfully applied regardless of the scanning pattern of the LiDAR. © 2025 IEEE.

Item Type:	Book Section
Uncontrolled Keywords:	Automated detection; Image processing - methods; Gluing; Autonomous Vehicles; road safety; autonomous driving; Real-time detection; Camera vision; Vehicle system; Road hazards; Speed bumps;
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Distributed Events Analysis Research Laboratory
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	11 Jul 2025 06:34
Last Modified:	11 Jul 2025 06:34
URI:	https://eprints.sztaki.hu/id/eprint/10946

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