  This paper describes the exploration and learnings during the process of
developing a self-driving algorithm in simulation, followed by deployment on a
real car. We specifically concentrate on the Formula Student Driverless
competition. In such competitions, a formula race car, designed and built by
students, is challenged to drive through previously unseen tracks that are
marked by traffic cones. We explore and highlight the challenges associated
with training a deep neural network that uses a single camera as input for
inferring car steering angles in real-time. The paper explores in-depth
creation of simulation, usage of simulations to train and validate the software
stack and then finally the engineering challenges associated with the
deployment of the system in real-world.
