Udacity Self-Driving Engineer Nanodegree. Term 3, assignment 1 — This is the summary of the implementation of a path planning algorithm for a self-driving car in a simulated highway. Frenet coordinates Transforming all the data points from Cartesian coordinates to Frenet coordinates shows to be really helpful especially in a highway settings. Although the transformation introduces approximation errors the Frenet coordinates…

Udacity Self-Driving Engineer Nanodegree. Term 2, assignment 4 — We’re now approaching the end of term 2 of the Udacity Self-Driving Engineer Nanodegree. This term has been all about hardcore robotics. It’s been a blast to learn how to build the core robotic functions of an autonomous vehicle system: sensor fusion, localization, and control. This last module was built…

Udacity Self-Driving Engineer Nanodegree. Term 2, assignment 3 — The assignment Build a PID controller and tune the PID hyperparameters by applying the general processing flow as described in the previous lessons. Test the solution on Udacity simulator. See how fast you get the vehicle to SAFELY go! The simulator data Udacity’s simulator app provides cross-track error CTE, speed, and steering angle data via…

Securing the CAN bus and dodging sensor spoofing are part of the ride to level 5 autonomy. — Since the very first webcam we’ve seen plenty of anecdotes of connected devices going awry. The snowballing of consumer and industrial IoT amplified the number of possible devices maliciously targeted. Hackers have taken control of devices, spied on people, disrupted businesses and governments, swarmed thousands of devices into botnets. …

Udacity Self-Driving Engineer Nanodegree — term 1, assignment 5. Goal This is the report created for the fifth and final assignment of the first term of Udacity Self-Driving Car Engineer Nanodegree. …

Computer vision techniques to implement an improved lane finding algorithm. — Goal This is the report created for the fourth assignment of the first term of Udacity Self-Driving Car Engineer Nanodegree. The challenge was to create a improved lane finding algorithm, using computer vision techniques. The core of the work is a software pipeline that identifies the lane boundaries in a video…

Goals The goal of this project was to utilize deep neural networks and convolutional neural networks in order to clone driving behavior. Udacity provided a simulator application where we can steer a car around a track for data collection. The collected data is comprised of image data captured from a car’s…

“Live as if you were to die tomorrow. Learn as if you were to live forever.” ― Mahatma Gandhi Deep learning is already present in search, comms, advertising, commerce, finance, medicine, media, and many other fields. Its reach will only increase in the years to come. When you realize that…

Meeting new entrepreneurs and helping them is probably one of the most rewarding things I do on a regular basis. Being a Techstars and Founder Institute mentor has put me in touch with an incredible diverse range of founders. …