mlsanityData to 14 Oct 2026

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

arXiv 2609.12660 ↗First posted 11 Sep 2026cs.RO cs.SY eess.SYcited by 0

On the map: End-to-end autonomous driving → Reinforcement learning for driving

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.

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.

Builds on

12 of 26 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing14 Feb

Other references · 11

  1. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
  2. 2025Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classificationcited by 26
  3. 2020HPIPM: a high-performance quadratic programming framework for model predictive controlcited by 23
  4. 2020Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarioscited by 14
  5. 2023Autonomous Drifting with 3 Minutes of Data via Learned Tire Modelscited by 7
  6. 2025Performance-driven Constrained Optimal Auto-Tuner for MPCcited by 6
  7. 2024Sampling-Based Motion Planning with Online Racing Line Generation for Autonomous Driving on Three-Dimensional Race Trackscited by 4
  8. 2024Trajectory Planning Using Tire Thermodynamics for Automated Driftingcited by 2
  9. 2024Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learningcited by 2
  10. 2023Model Predictive Contouring Control for Vehicle Obstacle Avoidance at the Limit of Handlingcited by 1
  11. 2025Fast and Realistic Automated Scenario Simulations and Reporting for an Autonomous Racing Stackcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
  2. 2021Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars
  3. 2025On learning racing policies with reinforcement learning
  4. 2026Deep Reinforcement-Learning-Guided Model Predictive Control for Preventing Overtakes in Autonomous Racing
  5. 2026Safe Overtaking for Autonomous Racing Using Hierarchical Optimization and Learning-Based Control ↗
  6. 2025Spatial Envelope MPC: High Performance Driving without a Reference
  7. 2022TC-Driver: Trajectory Conditioned Driving for Robust Autonomous Racing -- A Reinforcement Learning Approach
  8. 2025Context-Aware Model-Based Reinforcement Learning for Autonomous Racing