Comparative Analysis of Steering Angle Prediction For Automated Object
Using Deep Neural Network
Abstract
Deep learning’s rapid gains in automation are making it more popular in
a variety of complex jobs. The self-driving object is an emerging
technology that has the potential to transform the entire planet. The
steering control of an automated item is critical to ensuring a safe and
secure voyage. Consequently, in this study, we developed a methodology
for predicting the steering angle only by looking at the front images of
a vehicle. In addition, we used an Internet of Things-based system for
collecting front images and steering angles. A Raspberry Pi (RP) camera
is used in conjunction with a Raspberry Pi (RP) processing unit to
capture images from vehicles, and the RP processing unit is used to
collect the angles associated with each image. Apart from that, we’ve
made use of deep learning-based algorithms such as VGG16, ResNet-152,
DenseNet-201, and Nvidia’s models, all of which were trained using
labeled training data. Our models are End-to-End CNN models, which do
not require extracting elements from data such as roads, lanes, or other
objects before predicting steering angle. As a result of our comparative
investigation, we can conclude that the Nvidia model’s performance was
satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia
model outperforms the other pre-trained models, even though other models
work well.