Udacity SDC Nanodegree
This project is a part the second semester of Udacity’s Self-Driving Car Nanodegree. The goal is to apply the model predictive control (MPC) to the simulated car. The MPC is robust control algorithm, and it can be applied to systems with delays since MPC looks into the future. To reach the goal of driving a car, first, we go into the theory of MPC to see how it works and all its ingredients. After that, there is a word on the simplified bicycle model which was used to describe the kinematics of the car. …
When driving a car, two very important things to do is to a) stay in your lane b) avoid other cars. To do so we need to know where the road lane is and where the other cars are. Same story is for self-driving cars, thus the guys at Udacity decided that the last two projects of Self-Driving Car Nannodegree. In my previous post I have talked how to find lane lines, and make it robust against lighting changes and noise. …
The goal of the project was to find and highlight the road lane on the video recorded from the car. Once the lane was found there is additional requirement to calculate the curvature of the lane and position of the car relative to the lane. The project is a part of first term of Udacity’s Self-Driving Car Nanodegree. The whole pipeline was implemented in Python with the help of OpenCV library.
Before jumping to finding the lane itself, there is a bit of prep-work to be done. That includes: