Design and Simulation of Self-Driving Cars using Optimal and Machine Learning based control systems

The Ignited summer fellowship at Stanford enabled me to jumpstart my knowledge and curriculum development towards developing autonomous vehicles control systems. I created a set of lesson plans to walk through and teach high school computer science students to create software simulation environments for real-life road traffic as well as for a self-driving car to navigate terrain in a 2D environment.

Stanford’s Autonomous Systems Lab (ASL) is deeply involved in ML (Machine Learning) and AI in robotics and Autonomous control systems for Land, Air, and Spaceborne vehicles. Learning from experts at Stanford I was able to create a set of lesson plans that will enable students to design and simulate autonomous (self-driving) vehicles. With this exercise students will learn the concept of modeling real-life systems in software simulation, learn optimization strategies to minimize the cost and maximize the performance, and understand the concept of ML and its application to optimize trajectory of autonomous vehicles from point A to B along its route.