ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller

Haoxin Lin; Junjie Zhou; Daheng Xu; Yang Yu

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arXiv · 2604.04401

ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller

Haoxin Lin·Junjie Zhou·Daheng Xu·Yang Yu

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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.

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BibTeX RIS

Haoxin Lin, Junjie Zhou, Daheng Xu, Yang Yu. 2026-04-06. ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller. https://arxiv.org/abs/2604.04401

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