ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller
Abstract
Braking system, the key module to ensure the safety and steer-ability of current vehicles, relies on extensive manual calibration during production. Reducing labor and time consumption while maintaining the Vehicle Braking Controller (VBC) performance greatly benefits the vehicle industry. Model-based methods in offline reinforcement learning, which facilitate policy exploration within a data-driven dynamics model, offer a promising solution for addressing real-world control tasks. This work proposes ReinVBC, which applies an offline model-based reinforcement learning approach to deal with the vehicle braking control problem. We introduce useful engineering designs into the paradigm of model learning and utilization to obtain a reliable vehicle dynamics model and a capable braking policy. Several results demonstrate the capability of our method in real-world vehicle braking and its potential to replace the production-grade anti-lock braking system.
Cite
@article{arxiv.2604.04401,
title = {ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller},
author = {Haoxin Lin and Junjie Zhou and Daheng Xu and Yang Yu},
journal= {arXiv preprint arXiv:2604.04401},
year = {2026}
}