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 3

The assignment

  1. Build a PID controller and tune the PID hyperparameters by applying the general processing flow as described in the previous lessons.
  2. Test the solution on Udacity simulator.
  3. 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.

Freakout level as a new technology navigates the hype cycle

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. …

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…

Alberto Escarlate

Multi time founder, CTO and product executive. Founder @ridehugobus, EIR: @human_ventures & @techstars. VC: @collabfund

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