# MPC Builder for Autonomous Drive: Automatic Generation of MPCs for Motion Planning and Control

arXiv: https://arxiv.org/abs/2210.16512 · HTML: https://mlsanity.com/paper/2210.16512/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2022.

## Built on by (most cited first, 3 of 3)

- [Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning](https://mlsanity.com/paper/2312.16620/) (2023)
- [LVLM-MPC Collaboration for Autonomous Driving: A Safety-Aware and Task-Scalable Control Architecture](https://mlsanity.com/paper/2505.04980/) (2025)
- [Agentic MPC for Semantic Control System Resynthesis](https://mlsanity.com/paper/2606.12774/) (2026)

## Abstract (arXiv)

This study presents a new framework for vehicle motion planning and control based on the automatic generation of model predictive controllers (MPCs) named MPC Builder. In this framework, several components necessary for MPC, such as prediction models, constraints, and cost functions, are prepared in advance. The MPC Builder then generates various MPCs online in a unified manner according to traffic situations. This scheme enabled us to represent various driving tasks with less design effort than typical switched MPC systems. The proposed framework was implemented considering the continuation/generalized minimum residual (C/GMRES) method optimization solver, which can reduce computational costs. Finally, numerical experiments on multiple driving scenarios were presented.