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The Need of Advanced Driver-Assistance System’s Development based on an Analysis of Road Accidents

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  • Diana GHEORGHE

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.

Suggested Citation

  • Diana GHEORGHE, 2023. "The Need of Advanced Driver-Assistance System’s Development based on an Analysis of Road Accidents," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 27(3), pages 17-28.
    • Handle: RePEc:aes:infoec:v:27:y:2023:i:3:p:17-28
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    1. Cătălina-Lucia COCIANU & Hakob GRIGORYAN, 2016. "Machine Learning Techniques For Stock Market Prediction.Acase Study Of Omv Petrom," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(3), pages 63-82.
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