# Model- and Acceleration-based Pursuit Controller for High-Performance Autonomous Racing

arXiv: https://arxiv.org/abs/2209.04346 · HTML: https://mlsanity.com/paper/2209.04346/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2022.

## Built on by (most cited first, 13 of 13)

- [RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms](https://mlsanity.com/paper/2501.17311/) (2025)
- [Developing Path Planning with Behavioral Cloning and Proximal Policy Optimization for Path-Tracking and Static Obstacle Nudging](https://mlsanity.com/paper/2409.05289/) (2024)
- [Drive Fast, Learn Faster: On-Board RL for High Performance Autonomous Racing](https://mlsanity.com/paper/2505.07321/) (2025)
- [Steering Prediction via a Multi-Sensor System for Autonomous Racing](https://mlsanity.com/paper/2409.19356/) (2024)
- [Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing](https://mlsanity.com/paper/2312.06406/) (2023)
- [ReflexDiffusion: Reflection-Enhanced Trajectory Planning for High-lateral-acceleration Scenarios in Autonomous Driving](https://mlsanity.com/paper/2601.09377/) (2026)
- [Learning to Drift in Extreme Turning with Active Exploration and Gaussian Process Based MPC](https://mlsanity.com/paper/2410.05740/) (2024)
- [MMD-OPT : Maximum Mean Discrepancy Based Sample Efficient Collision Risk Minimization for Autonomous Driving](https://mlsanity.com/paper/2412.09121/) (2024)
- [DRIVE Through the Unpredictability:From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty](https://mlsanity.com/paper/2506.16593/) (2025)
- [Context-Aware Model-Based Reinforcement Learning for Autonomous Racing](https://mlsanity.com/paper/2510.11501/) (2025)
- [LLM-Driven Scenario-Aware Planning for Autonomous Driving](https://mlsanity.com/paper/2601.21876/) (2026)
- [Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform](https://mlsanity.com/paper/2607.24320/) (2026)
- [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)

Autonomous racing is a research field gaining large popularity, as it pushes autonomous driving algorithms to their limits and serves as a catalyst for general autonomous driving. For scaled autonomous racing platforms, the computational constraint and complexity often limit the use of Model Predictive Control (MPC). As a consequence, geometric controllers are the most frequently deployed controllers. They prove to be performant while yielding implementation and operational simplicity. Yet, they inherently lack the incorporation of model dynamics, thus limiting the race car to a velocity domain where tire slip can be neglected. This paper presents Model- and Acceleration-based Pursuit (MAP) a high-performance model-based trajectory tracking algorithm that preserves the simplicity of geometric approaches while leveraging tire dynamics. The proposed algorithm allows accurate tracking of a trajectory at unprecedented velocities compared to State-of-the-Art (SotA) geometric controllers. The MAP controller is experimentally validated and outperforms the reference geometric controller four-fold in terms of lateral tracking error, yielding a tracking error of 0.055m at tested speeds up to 11m/s.