A Comparison on Machine Learning Classifiers related to Traffic Sign Recognition
Jiajin Zhang · Applied and Computational Engineering · 2023
With the rapid development of machine learning and the automotive industry, the industry of autonomous driving continues to grow. At the same time, governments have new regulations on autonomous driving, which tells us that reliable systems have become more and more critical while developing autonomous driving. In this paper, I use three different classifiers, which are Logistic Regression (LR), Random Forest (RF), and neural network (Multilayer Perceptron), to do the traffic sign recognition tasks and set the best parameters for every classifier. I train three classifiers with the best parameters and estimate using cross-value methods. Finally, I compared the performance, which indicates Random Forest Classifier has the best result among the three classifiers.