Si-yeon Jang, Hye-lim Choi, Yun-ju Oh, Journal of Internet Computing and Services, Vol. 25, No. 2, pp. 21-28, Apr. 2024
10.7472/jksii.2024.25.2.21, Full Text:  HTML
Keywords: Automonous driving, Performance prediction, Radar, Stacking Ensemble

Abstract

Radar is an essential sensor component in autonomous vehicles, and the market for radar applications in this context is steadily expanding with a growing variety of products. In this study, we aimed to enhance the stability and performance of radar systems by developing and evaluating a radar performance prediction model that can predict radar defects. We selected seven machine learning and deep learning algorithms and trained the model with a total of 49 input data types. Ultimately, when we employed an ensemble of 17 models, it exhibited the highest performance. We anticipate that these research findings will assist in predicting product defects at the production stage, thereby maximizing production yield and minimizing the costs associated with defective products.

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