Data-Driven Analysis of Crash Patterns in SAE Level 2 and Level 4 Automated Vehicles Using K-means Clustering and Association Rule Mining

This article presents a data-driven approach to analyze crash patterns in automated vehicles. The use of K-means clustering and association rule mining is a solid methodology for identifying significant patterns. The focus on SAE Level 2 and Level 4 vehicles is relevant to current industry trends. However, the article's depth and the specific datasets used are unknown without access to the full text. The effectiveness of the analysis depends heavily on the quality and comprehensiveness of the data.

• •Applies data mining techniques (K-means, association rule mining) to analyze crash patterns.
• •Focuses on SAE Level 2 and Level 4 automated vehicles.
• •Aims to identify significant patterns in crash data.