# Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits

arXiv: https://arxiv.org/abs/2202.03807 · HTML: https://mlsanity.com/paper/2202.03807/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 13 of 13)

- [Optimization-Based Autonomous Racing of 1:43 Scale RC Cars](https://mlsanity.com/paper/1711.07300/) (2017)
- [AMZ Driverless: The Full Autonomous Racing System](https://mlsanity.com/paper/1905.05150/) (2019)
- [Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space](https://mlsanity.com/paper/2102.09812/) (2021)
- [A Non-Cooperative Game Approach to Autonomous Racing](https://mlsanity.com/paper/1712.03913/) (2017)
- [Learning How to Autonomously Race a Car: a Predictive Control Approach](https://mlsanity.com/paper/1901.08184/) (2019)
- [Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios](https://mlsanity.com/paper/2005.08664/) (2020)
- [Optimization-Based Hierarchical Motion Planning for Autonomous Racing](https://mlsanity.com/paper/2003.04882/) (2020)
- [DeepRacing: Parameterized Trajectories for Autonomous Racing](https://mlsanity.com/paper/2005.05178/) (2020)
- [The Autonomous Racing Software Stack of the KIT19d](https://mlsanity.com/paper/2010.02828/) (2020)
- [Exploring the Capabilities and Limits of 3D Monocular Object Detection -- A Study on Simulation and Real World Data](https://mlsanity.com/paper/2005.07424/) (2020)
- [Real-Time Adaptive Velocity Optimization for Autonomous Electric Cars at the Limits of Handling](https://mlsanity.com/paper/2012.13586/) (2020)
- [Persistent Map Saving for Visual Localization for Autonomous Vehicles: An ORB-SLAM Extension](https://mlsanity.com/paper/2005.07429/) (2020)
- [Safe Motion Planning for Autonomous Driving using an Adversarial Road Model](https://mlsanity.com/paper/2005.07691/) (2020)

## Built on by (most cited first, 11 of 11)

- [MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts](https://mlsanity.com/paper/2303.00638/) (2023)
- [A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing](https://mlsanity.com/paper/2209.15073/) (2022)
- [Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Constrained Neural Network for Autonomous Racing](https://mlsanity.com/paper/2312.04374/) (2023)
- [Uncertainty-Aware Autonomous Vehicles: Predicting the Road Ahead](https://mlsanity.com/paper/2510.22680/) (2025)
- [Fast and Modular Autonomy Software for Autonomous Racing Vehicles](https://mlsanity.com/paper/2408.15425/) (2024)
- [Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes](https://mlsanity.com/paper/2306.03405/) (2023)
- [Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning](https://mlsanity.com/paper/2308.13491/) (2023)
- [Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing](https://mlsanity.com/paper/2312.06406/) (2023)
- [Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation](https://mlsanity.com/paper/2409.19647/) (2024)
- [Spatial Envelope MPC: High Performance Driving without a Reference](https://mlsanity.com/paper/2509.18506/) (2025)
- [Expert Knowledge-driven Reinforcement Learning for Autonomous Racing via Trajectory Guidance and Dynamics Constraints](https://mlsanity.com/paper/2603.05842/) (2026)

## Abstract (arXiv)

Motorsport has always been an enabler for technological advancement, and the same applies to the autonomous driving industry. The team TUM Auton-omous Motorsports will participate in the Indy Autonomous Challenge in Octo-ber 2021 to benchmark its self-driving software-stack by racing one out of ten autonomous Dallara AV-21 racecars at the Indianapolis Motor Speedway. The first part of this paper explains the reasons for entering an autonomous vehicle race from an academic perspective: It allows focusing on several edge cases en-countered by autonomous vehicles, such as challenging evasion maneuvers and unstructured scenarios. At the same time, it is inherently safe due to the motor-sport related track safety precautions. It is therefore an ideal testing ground for the development of autonomous driving algorithms capable of mastering the most challenging and rare situations. In addition, we provide insight into our soft-ware development workflow and present our Hardware-in-the-Loop simulation setup. It is capable of running simulations of up to eight autonomous vehicles in real time. The second part of the paper gives a high-level overview of the soft-ware architecture and covers our development priorities in building a high-per-formance autonomous racing software: maximum sensor detection range, relia-ble handling of multi-vehicle situations, as well as reliable motion control under uncertainty.