# AMZ Driverless: The Full Autonomous Racing System

arXiv: https://arxiv.org/abs/1905.05150 · HTML: https://mlsanity.com/paper/1905.05150/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2019.

## Built on by (most cited first, 29 of 29)

- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing](https://mlsanity.com/paper/2103.04909/) (2021)
- [Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning](https://mlsanity.com/paper/2107.08325/) (2021)
- [Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits](https://mlsanity.com/paper/2202.03807/) (2022)
- [When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL](https://mlsanity.com/paper/2406.01163/) (2024)
- [TC-Driver: Trajectory Conditioned Driving for Robust Autonomous Racing -- A Reinforcement Learning Approach](https://mlsanity.com/paper/2205.09370/) (2022)
- [Bridging the Sim-to-Real Gap with Bayesian Inference](https://mlsanity.com/paper/2403.16644/) (2024)
- [SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer](https://mlsanity.com/paper/2509.18648/) (2025)
- [SOMBRL: Scalable and Optimistic Model-Based RL](https://mlsanity.com/paper/2511.20066/) (2025)
- [Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning](https://mlsanity.com/paper/2311.07558/) (2023)
- [Performance-driven Constrained Optimal Auto-Tuner for MPC](https://mlsanity.com/paper/2503.07127/) (2025)
- [Safe Exploration via Policy Priors](https://mlsanity.com/paper/2601.19612/) (2026)
- [Uncertainty-Aware Autonomous Vehicles: Predicting the Road Ahead](https://mlsanity.com/paper/2510.22680/) (2025)
- [Zero-Order Optimization for Gaussian Process-based Model Predictive Control](https://mlsanity.com/paper/2211.15522/) (2022)
- [A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential](https://mlsanity.com/paper/2312.14808/) (2023)
- [NeoRL: Efficient Exploration for Nonepisodic RL](https://mlsanity.com/paper/2406.01175/) (2024)
- [er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds](https://mlsanity.com/paper/2310.18112/) (2023)
- [Real-Time Optimal Trajectory Planning for Autonomous Vehicles and Lap Time Simulation Using Machine Learning](https://mlsanity.com/paper/2102.02315/) (2021)
- [Fast and Modular Autonomy Software for Autonomous Racing Vehicles](https://mlsanity.com/paper/2408.15425/) (2024)
- [TurboMPC: Fast, Scalable, and Differentiable Model Predictive Control on the GPU](https://mlsanity.com/paper/2606.24039/) (2026)
- [Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems](https://mlsanity.com/paper/2301.01470/) (2023)
- [Deep Reinforcement Learning for Local Path Following of an Autonomous Formula SAE Vehicle](https://mlsanity.com/paper/2401.02903/) (2024)
- [On learning racing policies with reinforcement learning](https://mlsanity.com/paper/2504.02420/) (2025)
- [Safe and Near-Optimal Control with Online Dynamics Learning](https://mlsanity.com/paper/2509.16650/) (2025)
- [TARC: Time-Adaptive Robotic Control](https://mlsanity.com/paper/2510.23176/) (2025)
- [MODISSA: a multipurpose platform for the prototypical realization of vehicle-related applications using optical sensors](https://mlsanity.com/paper/2105.13580/) (2021)
- [An Evolutionary Game-Theoretic Merging Decision-Making Considering Social Acceptance for Autonomous Driving](https://mlsanity.com/paper/2508.07080/) (2025)
- [Simulation Priors for Data-Efficient Deep Learning](https://mlsanity.com/paper/2509.05732/) (2025)
- [A Modular Architecture Design for Autonomous Driving Racing in Controlled Environments](https://mlsanity.com/paper/2512.03886/) (2025)

## Abstract (arXiv)

This paper presents the algorithms and system architecture of an autonomous racecar. The introduced vehicle is powered by a software stack designed for robustness, reliability, and extensibility. In order to autonomously race around a previously unknown track, the proposed solution combines state of the art techniques from different fields of robotics. Specifically, perception, estimation, and control are incorporated into one high-performance autonomous racecar. This complex robotic system, developed by AMZ Driverless and ETH Zurich, finished 1st overall at each competition we attended: Formula Student Germany 2017, Formula Student Italy 2018 and Formula Student Germany 2018. We discuss the findings and learnings from these competitions and present an experimental evaluation of each module of our solution.