Mitigating Bias in Bayesian Optimized Data While Designing MacPherson Suspension Architecture

Abstract

With the rise of artificial intelligence, the automotive industry searched for novel ways to improve future product design. We focus on designing automatic MacPherson suspension architecture for the automotive sector. It takes time for an automotive engineer to design vehicle parts and thus slows the pace of innovation in this field. Given the car's particular kinematic characteristics, we propose to predict an architecture by positioning the hardpoints. This work deals with the biased data generated using the discipline models using the dataset shift learning paradigm. The optimized data are created with random and uniform sampling, with more samples with random sampling. We resolve the bias in the data, using a novel criterion for tuning the kernel mean matching and a weight estimation algorithm and designing the required target characteristics.