Automatic Sensing, Perception, and Prediction for Intelligent Vehicles Using Deep Learning
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science and Info Sys
Date of Award
Fall 2021
Abstract
In this thesis, we enabled automatic sensing for intelligent vehicles that utilize their perception ability through sensors to predict correct driving behaviors for safe driving. First, we focused on developing deep learning models that learn from real world driving data collected by driving on local roads and on highways. The models are trained, tested, and applied to predict the steering angle of vehicles. Specifically, we leveraged convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to develop deep learning models that make use of real driving streaming data and respective steering angles to learn. We also studied the literature to build motion prediction and path planning modules that can be deployed to assist driving and promote safe driving. Preliminary experiments were conducted and experimental data is collected to examine the performance of the selected motion prediction and planning algorithms. We then discussed our future work at the end of this thesis.
Advisor
Yuehua Wang
Subject Categories
Computer Sciences | Physical Sciences and Mathematics
Recommended Citation
Ijaz, Nosherwan, "Automatic Sensing, Perception, and Prediction for Intelligent Vehicles Using Deep Learning" (2021). Electronic Theses & Dissertations. 533.
https://digitalcommons.tamuc.edu/etd/533
