# Driving Context-guided Model Predictive Planning and Control for Autonomous Car Racing at the Limit and Beyond

arXiv: https://arxiv.org/abs/2609.12660 · HTML: https://mlsanity.com/paper/2609.12660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 12 of 12)

- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification](https://mlsanity.com/paper/2502.02133/) (2025)
- [HPIPM: a high-performance quadratic programming framework for model predictive control](https://mlsanity.com/paper/2003.02547/) (2020)
- [Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios](https://mlsanity.com/paper/2005.08664/) (2020)
- [Autonomous Drifting with 3 Minutes of Data via Learned Tire Models](https://mlsanity.com/paper/2306.06330/) (2023)
- [Performance-driven Constrained Optimal Auto-Tuner for MPC](https://mlsanity.com/paper/2503.07127/) (2025)
- [Sampling-Based Motion Planning with Online Racing Line Generation for Autonomous Driving on Three-Dimensional Race Tracks](https://mlsanity.com/paper/2403.18643/) (2024)
- [Trajectory Planning Using Tire Thermodynamics for Automated Drifting](https://mlsanity.com/paper/2407.12989/) (2024)
- [Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning](https://mlsanity.com/paper/2412.03803/) (2024)
- [Model Predictive Contouring Control for Vehicle Obstacle Avoidance at the Limit of Handling](https://mlsanity.com/paper/2308.06742/) (2023)
- [Fast and Realistic Automated Scenario Simulations and Reporting for an Autonomous Racing Stack](https://mlsanity.com/paper/2512.24402/) (2025)

## Abstract (arXiv)

This paper presents a Model Predictive Control-based motion planning and control pipeline for autonomous car racing capable of adapting to different driving contexts, such as overtaking, nominal driving, and countersteering. A Cost Blending state machine manages the identification of different driving contexts and the selection of their predefined weights to be applied to the Model Predictive Planning (MPP) and Control (MPC) modules. The two optimization-based solutions share the same problem formulation and model, differing only in horizon length, rate, tuning, and in their open-loop versus closed-loop approach to maximize the effectiveness of their interaction. The work is validated on the fully autonomous open-wheel racecar Superformula EAV-25, with a lap time achieved that is within 2% of the best human driver reference. The results demonstrate the capability of the solution in driving at the limit of handling, smoothly executing overtaking maneuvers, and quickly reacting to high oversteering conditions to recover the vehicle stability.