Around September of the year 2016, Udacity announced a one-of-its-kind program. The program spanned over almost 10 months and promised to teach you the basics of one of the most interesting and exciting technology in the industry. It was designed by some of the pioneers in the field, like Prof. Sebastian Thrun, and was offered online, in the comfort and convenience of your home. The course had also bagged industry partnerships with Nvidia and Mercedes among others. The program was the Self Driving Car Engineer Nanodegree and it required proficiency in the basics of programming and machine learning to be eligible for enrollment.
Without wasting a minute, I logged into my Udacity account and registered for the course. I had already completed a lot of online courses on various topics of my interest and the Nanodegree seemed like a great place to not only learn about the amazing technologies behind the autonomous vehicles, but also get an experience with designing my own self driving car. The course promised to give the students an opportunity to run their final project on a real vehicle by implementing various functionalities like Drive-by-Wire, Traffic Light Detection and Classification, Steering, Path Planning, etc. I was selected for the November cohort of the course and I officially received my access on November 29, 2016.
My Advanced Lane Detection Project from Term 1
Today, three months after completing my Nanodegree, I look back at the course as one of the best investments of my time and money. The course lectures were very well designed and structured. The three terms of the nano-degree were meticulously planned. The first term introduced the concepts of Computer Vision and Deep Learning. The projects involved a lot of scripting with Python and TensorFlow to solve the problems like Lane and Curvature Detection, Vehicle Detection, Steering Angle prediction, etc. The application oriented nature of the projects made it even more interesting.
My Vehicle Detection Project from Term 1
Term 2 was focused on the control side of things. It covered the topics of Sensor Fusion, Localization and Control. This term was heavily dominated by C++ and Algebra. The projects included implementing Extended and Unscented Kalman filters for tracking non-linear motion, Localization using Markov and Particle Filter and Model Predictive Control to drive the vehicle around the track. I learnt many new things in this term, from C++ programming to the mathematics behind the working of Kalman Filter, Particle Filter and MPC to their algorithmic implementations.
My Model Predictive Controller project from Term 2
The final term was focused on stitching together the various topics that were taught and applying them to create your own autonomous vehicle. The topics included path planning, semantic segmentation (or scene understanding), functional safety and finally the capstone project.
My Path Planning project from Term 3
What set the entire nano-degree apart from the other courses was it novelty. There is no other course out there that can teach you so much in such a short amount of time and in so much depth. The course also provided me with a collated set of resources for learning. Apart from the well-designed lecture videos, quizzes and projects, one of the most rewarding experiences was interaction with people from around the world. Everyone who was taking the course was excited and eager to share his/her knowledge and help others. The Slack and the Udacity discussion forums are full of activities. I interacted with people from around the world, from USA to Germany, to Japan. I discussed the projects and lectures with people from different academic and professional backgrounds, from a freshman to a Vice President of Engineering. These interactions not only helped me to create a world-wide network but also opened my eyes to the opportunities that are present around me. I also got an opportunity to explore some of the open courses like Stanford’s CS231n, the materials for which are freely available online. The amazing support of my peers and mentors played a huge role in helping me to master the material.
The nano-degree took a lot of time and effort to complete. Since I also pursued the optional material, which were mostly research papers, it took me more than average time for completion. However, the effect of the course was so profound, that I still go back to the material for revision, interact with new students on Slack and discuss the projects over WhatsApp. The course changed the way I approach the problems provided me with a solid base for future research. I hope that Udacity launches a more advanced version of the course soon.
My implementation for one of the Term 3 optional projects — Object Detection with R-FCN