# Adaptive MPC for Autonomous Lane Keeping

arXiv: https://arxiv.org/abs/1806.04335 · HTML: https://mlsanity.com/paper/1806.04335/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering](https://mlsanity.com/paper/2103.00345/) (2021)
- [On Architectures for Combining Reinforcement Learning and Model Predictive Control with Runtime Improvements](https://mlsanity.com/paper/2510.03354/) (2025)

## Abstract (arXiv)

This paper proposes an Adaptive Robust Model Predictive Control strategy for lateral control in lane keeping problems, where we continuously learn an unknown, but constant steering angle offset present in the steering system. Longitudinal velocity is assumed constant. The goal is to minimize the outputs, which are distance from lane center line and the steady state heading angle error, while satisfying respective safety constraints. We do not assume perfect knowledge of the vehicle lateral dynamics model and estimate and adapt in real-time the maximum possible bound of the steering angle offset from data using a robust Set Membership Method based approach. Our approach is even well-suited for scenarios with sharp curvatures on high speed, where obtaining a precise model bias for constrained control is difficult, but learning from data can be helpful. We ensure persistent feasibility using a switching strategy during change of lane curvature. The proposed methodology is general and can be applied to more complex vehicle dynamics problems.