Comparative Study of Unsupervised Clustering Methods Used for RADAR Applications

2024-26-0029

01/16/2024

Features
Event
Symposium on International Automotive Technology
Authors Abstract
Content
Driver safety has become an important aspect. To have driver safety RADAR is an essential part of vehicles hence RADAR has great significance in the automotive industry. The Radar sensor collects data from surroundings that may have unwanted data that may lead to improper detections of intended objects, so to have proper object detections it is needed to use clustering methods on the radar point cloud data. There are numerous unsupervised clustering methods used for RADAR applications. In this paper, the comparisons of different unsupervised algorithms such as K-Means Clustering, Hierarchical Clustering, Cluster Using the Gaussian Mixture Model, and DBSCAN are presented. All these clustering algorithms are evaluated based on various evaluation criteria such as the Silhouette coefficient, Davies Bouldin index, etc. Based on evaluations and comparative studies applications of the clustering algorithms are classified.
Meta TagsDetails
DOI
https://doi.org/10.4271/2024-26-0029
Pages
8
Citation
Prajapati, M., Payghan, V., Chauhan, A., and Nidubrolu, K., "Comparative Study of Unsupervised Clustering Methods Used for RADAR Applications," SAE Technical Paper 2024-26-0029, 2024, https://doi.org/10.4271/2024-26-0029.
Additional Details
Publisher
Published
Jan 16
Product Code
2024-26-0029
Content Type
Technical Paper
Language
English