# Fast and Modular Autonomy Software for Autonomous Racing Vehicles

arXiv: https://arxiv.org/abs/2408.15425 · HTML: https://mlsanity.com/paper/2408.15425/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 63 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 16 of 16)

- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2203.08195/) (2022)
- [A Survey of Deep Learning Applications to Autonomous Vehicle Control](https://mlsanity.com/paper/1912.10773/) (2019)
- [TorchSparse: Efficient Point Cloud Inference Engine](https://mlsanity.com/paper/2204.10319/) (2022)
- [AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics](https://mlsanity.com/paper/2008.08063/) (2020)
- [Real-time Object Detection for Streaming Perception](https://mlsanity.com/paper/2203.12338/) (2022)
- [AMZ Driverless: The Full Autonomous Racing System](https://mlsanity.com/paper/1905.05150/) (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)
- [Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving](https://mlsanity.com/paper/2011.00462/) (2020)
- [Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits](https://mlsanity.com/paper/2202.03807/) (2022)
- [Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds](https://mlsanity.com/paper/2207.11136/) (2022)
- [Robust Modeling and Controls for Racing on the Edge](https://mlsanity.com/paper/2205.10841/) (2022)

## Built on by (most cited first, 2 of 2)

- [Generative AI Agents in Autonomous Machines: A Safety Perspective](https://mlsanity.com/paper/2410.15489/) (2024)
- [A Multi-Modal Perception Pipeline for Object Detection and Tracking in Autonomous Racing](https://mlsanity.com/paper/2609.08338/) (2026)

## Abstract (arXiv)

Autonomous motorsports aim to replicate the human racecar driver with software and sensors. As in traditional motorsports, Autonomous Racing Vehicles (ARVs) are pushed to their handling limits in multi-agent scenarios at extremely high (≥ 150mph) speeds. This Operational Design Domain (ODD) presents unique challenges across the autonomy stack. The Indy Autonomous Challenge (IAC) is an international competition aiming to advance autonomous vehicle development through ARV competitions. While far from challenging what a human racecar driver can do, the IAC is pushing the state of the art by facilitating full-sized ARV competitions. This paper details the MIT-Pitt-RW Team's approach to autonomous racing in the IAC. In this work, we present our modular and fast approach to agent detection, motion planning and controls to create an autonomy stack. We also provide analysis of the performance of the software stack in single and multi-agent scenarios for rapid deployment in a fast-paced competition environment. We also cover what did and did not work when deployed on a physical system the Dallara AV-21 platform and potential improvements to address these shortcomings. Finally, we convey lessons learned and discuss limitations and future directions for improvement.