Applied Machine Learning: Autonomous Navigation
Channel: JMU ISAT
Date: 2018-09-23

Full Title: Applied Machine Learning: Autonomous Navigation
Presenter: James Cooper
Advised by: Anthony Teate
Abstract: Although humans have been driving vehicles for over 100 years, driving remains one of the most dangerous activities we perform on a daily basis, resulting in the second highest cause of injury among all age groups worldwide. However, with recent advancements in artificial intelligence and software engineering, tech giants such as Google, Tesla, and others are developing and testing autonomous driving capabilities to begin phasing out the most dangerous aspect of modernday driving, the drivers. Autonomous capabilities are revolutionizing the automotive industry by limiting the prevalence of dangerous driving, such as drunk and distracted driving, making the roads a safer place for everyone. This project was designed to replicate recent autonomous driving advances, at a much smaller scale, by building and developing our own intelligent navigational software using various machine learning techniques within Google’s TensorFlow software. Our robotic car will be equipped with open-source computer vision, OpenCV, along with various other sensors to help quickly identify obstacles and safely navigate the car’s path in real-time. Later, we hope to upload this serialized software onto the cloud using Amazon Web Services so our cars can communicate beyond the limits of our localized servers. This project was not meant to duplicate full-scale autonomous navigation, but rather to serve as a miniaturized demonstration of the applicable power of machine learning and Google’s TensorFlow software package.