Honors College Theses
Publication Date
4-17-2019
Major
Mechanical Engineering (B.S.)
Document Type and Release Option
Thesis (open access)
Faculty Mentor
Dr. Biswanath Samanta
Abstract
End to End learning is a method of deep learning which has been used to great effect to solve complex problems which would normally be performed by humans. Within this thesis, a neural network was created to mimic the steering patterns of humans in highway driving situations. A Turtlebot was used in place of a car and was tested within a laboratory on a closed loop track to drive within the lanes created for it. The network architecture was based on that of Nvidia’s model which was used for predicting steering angles of a vehicle. The network was successfully trained and implemented, however showed poor performance, under fitting the predictions to a single value for all tests performed on it. This error is most likely the result of inconsistent and unclear data, causing the network to fail to recognize any pattern between steering commands and image features.
Recommended Citation
Russell, Keith, "End-to-End Learning: Using Neural Networks for Vehicle Control and Obstacle Avoidance" (2019). Honors College Theses. 417.
https://digitalcommons.georgiasouthern.edu/honors-theses/417
