# Explorations and Lessons Learned in Building an Autonomous Formula SAE Car from Simulations

## TL;DR

This paper details the development of a self-driving algorithm for a Formula SAE car, emphasizing simulation training, real-world deployment challenges, and lessons learned in creating an autonomous racing vehicle.

## Contribution

It presents a comprehensive approach to building an autonomous racing car from simulation to real-world deployment, highlighting practical challenges and solutions.

## Key findings

- Successful simulation-based training of neural networks for steering
- Identification of key challenges in real-world deployment
- Insights into bridging simulation and real-world performance

## Abstract

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.