# Design of an Autonomous Racecar: Perception, State Estimation and System Integration

arXiv: https://arxiv.org/abs/1804.03252 · HTML: https://mlsanity.com/paper/1804.03252/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2018.

## Built on by (most cited first, 3 of 3)

- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles](https://mlsanity.com/paper/2301.10384/) (2023)
- [Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing](https://mlsanity.com/paper/2312.06406/) (2023)

## Abstract (arXiv)

This paper introduces fl"uela driverless: the first autonomous racecar to win a Formula Student Driverless competition. In this competition, among other challenges, an autonomous racecar is tasked to complete 10 laps of a previously unknown racetrack as fast as possible and using only onboard sensing and computing. The key components of fl"uela's design are its modular redundant sub-systems that allow robust performance despite challenging perceptual conditions or partial system failures. The paper presents the integration of key components of our autonomous racecar, i.e., system design, EKF-based state estimation, LiDAR-based perception, and particle filter-based SLAM. We perform an extensive experimental evaluation on real-world data, demonstrating the system's effectiveness by outperforming the next-best ranking team by almost half the time required to finish a lap. The autonomous racecar reaches lateral and longitudinal accelerations comparable to those achieved by experienced human drivers.