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

arXiv: https://arxiv.org/abs/1905.05940 · HTML: https://mlsanity.com/paper/1905.05940/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 6 of 6)

- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions](https://mlsanity.com/paper/2103.02760/) (2021)
- [Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation](https://mlsanity.com/paper/2511.09895/) (2025)
- [Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing](https://mlsanity.com/paper/2201.03246/) (2022)
- [Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car](https://mlsanity.com/paper/2308.13088/) (2023)
- [Deep Reinforcement Learning for Local Path Following of an Autonomous Formula SAE Vehicle](https://mlsanity.com/paper/2401.02903/) (2024)

## Abstract (arXiv)

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.