 The Need of Advanced Driver-Assistance System’s Development based on an Analysis of Road AccidentsDiana Gheorghe () Informatica Economica, 2023, vol. 27, issue 3, 17-28 Abstract: aking a look at the numbers of road accidents using Machine Learning techniques and configuring predictions based on historic data, the paper aims to emphasize the need of a method to decrease numbers of accidents. Machine learning techniques have shown great potential in analyzing large-scale datasets related to road accidents. By leveraging these techniques, researchers have been able to identify key contributing factors, such as driver behavior, road conditions, and vehicle characteristics, which play a crucial role in accident occurrence. Through the analysis of historical accident data, machine learning models can effectively predict the likelihood of future accidents and identify high-risk areas, enabling proactive measures to be implemented. ADAS systems provide real-time information and assist drivers in making informed decisions while driving, thereby mitigating potential risks. A particular interest of this Article is underlining the importance of ADAS in the automotive field and how it can benefit the drivers. Keywords: Machine Learning; ADAS; Road accidents; Automotive Industry (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:aes:infoec:v:27:y:2023:i:3:p:17-28 Access Statistics for this article Informatica Economica is currently edited by Ion Ivan More articles in Informatica Economica from Academy of Economic Studies - Bucharest, Romania Contact information at EDIRC. |
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