Abstract
The recently developed technology in autos makes traffic signal prediction
devices obligatory. It teaches users how to drive safely and manoeuvre their vehicles
effectively. Due to drivers' various forms of attention, the number of accidents is rising
alarmingly nowadays. The danger of distracted driving, which causes accidents, is
decreased thanks to this technology, which also assists in identifying and providing
information based on data. The notion of machine learning is presented, and the
concepts of supervised learning, unsupervised learning, and reinforcement learning are
covered under the heading of categorization and serve as the main principle. Linear
regression, neural networks, naive Bayes, random forests, support vector machines,
clustering, etc. are some types of models that machine learning may give. This study
describes how to train a model using machine learning, with the basic principle being
to divide the data into training, testing, and validation. The last section of this chapter
discusses how to access machine learning methods to improve the quality of a machine
learning project. The suggested approach provides an explanation of the combined
model of the modern convolutional neural network (CNN) and the classic support
vector machine (SVM) for traffic sign identification. Essentially, a CNN model was
trained to produce this model. Several CNN model designs, including LeNet, AlexNet,
and ResNet-50, may be used here. The subsequent layers of CNN's output may be
utilised as features. These characteristics were added to SVM for categorization
purposes.