# Game-Theoretic Model Predictive Control with Data-Driven Identification of Vehicle Model for Head-to-Head Autonomous Racing

arXiv: https://arxiv.org/abs/2106.04094 · HTML: https://mlsanity.com/paper/2106.04094/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2021.

## Built on by (most cited first, 7 of 7)

- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds](https://mlsanity.com/paper/2207.11136/) (2022)
- [AARK: An Open Toolkit for Autonomous Racing Research](https://mlsanity.com/paper/2410.00358/) (2024)
- [Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems](https://mlsanity.com/paper/2301.01470/) (2023)
- [Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning](https://mlsanity.com/paper/2308.13491/) (2023)
- [Game-theoretic Objective Space Planning](https://mlsanity.com/paper/2209.07758/) (2022)
- [Toward the Cognitive--Physical Limits of Embodied Intelligence through a World-Model-Centric Autonomous Racing Agent](https://mlsanity.com/paper/2608.10618/) (2026)

## Abstract (arXiv)

Resolving edge-cases in autonomous driving, head-to-head autonomous racing is getting a lot of attention from the industry and academia. In this study, we propose a game-theoretic model predictive control (MPC) approach for head-to-head autonomous racing and data-driven model identification method. For the practical estimation of nonlinear model parameters, we adopted the hyperband algorithm, which is used for neural model training in machine learning. The proposed controller comprises three modules: 1) game-based opponents' trajectory predictor, 2) high-level race strategy planner, and 3) MPC-based low-level controller. The game-based predictor was designed to predict the future trajectories of competitors. Based on the prediction results, the high-level race strategy planner plans several behaviors to respond to various race circumstances. Finally, the MPC-based controller computes the optimal control commands to follow the trajectories. The proposed approach was validated under various racing circumstances in an official simulator of the Indy Autonomous Challenge. The experimental results show that the proposed method can effectively overtake competitors, while driving through the track as quickly as possible without collisions.