# Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems

arXiv: https://arxiv.org/abs/2301.01470 · HTML: https://mlsanity.com/paper/2301.01470/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 4 Jan 2023.

## Builds on (most cited first, 5 of 5)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [AMZ Driverless: The Full Autonomous Racing System](https://mlsanity.com/paper/1905.05150/) (2019)
- [Game-Theoretic Model Predictive Control with Data-Driven Identification of Vehicle Model for Head-to-Head Autonomous Racing](https://mlsanity.com/paper/2106.04094/) (2021)
- [Robust Modeling and Controls for Racing on the Edge](https://mlsanity.com/paper/2205.10841/) (2022)
- [A Resilient Navigation and Path Planning System for High-speed Autonomous Race Car](https://mlsanity.com/paper/2207.12232/) (2022)

## Built on by (most cited first, 1 of 1)

- [Analyzing the Impact of Simulation Fidelity on the Evaluation of Autonomous Driving Motion Control](https://mlsanity.com/paper/2602.07984/) (2026)

## Abstract (arXiv)

In this letter, we propose a model parameter identification method via a hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient explore-exploit strategy to identify the parameters of dynamic models in a data-driven optimization manner. We utilize our method for model parameter identification of the AV-21, a full-scaled autonomous race vehicle. We then incorporate the optimized parameters for the design of model-based planning and control systems of our platform. In experiments, MI-HPO exhibits more than 13 times faster convergence than traditional parameter identification methods. Furthermore, the parametric models learned via MI-HPO demonstrate good fitness to the given datasets and show generalization ability in unseen dynamic scenarios. We further conduct extensive field tests to validate our model-based system, demonstrating stable obstacle avoidance and high-speed driving up to 217 km/h at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The source code for our work and videos of the tests are available at https://github.com/hynkis/MI-HPO.