# Learning-Based MPC for Fuel Efficient Control of Autonomous Vehicles with Discrete Gear Selection

arXiv: https://arxiv.org/abs/2503.11359 · HTML: https://mlsanity.com/paper/2503.11359/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2025.

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

- [Reinforcement Learning with Distributed MPC for Fuel-Efficient Platoon Control with Discrete Gear Transitions](https://mlsanity.com/paper/2601.18294/) (2026)
- [Bridging RL and MPC for mixed-integer optimal control with application to Formula 1 race strategies](https://mlsanity.com/paper/2604.00826/) (2026)

## Abstract (arXiv)

Co-optimization of both vehicle speed and gear position via model predictive control (MPC) has been shown to offer benefits for fuel-efficient autonomous driving. However, optimizing both the vehicle's continuous dynamics and discrete gear positions may be too computationally intensive for a real-time implementation. This work proposes a learning-based MPC scheme to address this issue. A policy is trained to select and fix the gear positions across the prediction horizon of the MPC controller, leaving a significantly simpler continuous optimization problem to be solved online. In simulation, the proposed approach is shown to have a significantly lower computation burden and a comparable performance, with respect to pure MPC-based co-optimization.