The Development of the Advanced Driver-Assistance System by Analyzing the Road Accidents

Diana Gheorghe ()

Informatica Economica, 2025, vol. 29, issue 2, 68-82

Abstract: This paper aims to emphasize the need for a method to decrease the number of accidents by examining the number of road accidents using Machine Learning techniques and configuring predictions based on historical data. 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. This article's particular interest is underlining the importance of ADAS in the automotive field and how it can benefit drivers.

Keywords: Machine Learning; Random Forest; PSO; ADAS; Road Accidents; Automotive Industry (search for similar items in EconPapers)
Date: 2025
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