[2604.04401] ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller

Computer Science > Robotics arXiv:2604.04401 (cs) [Submitted on 6 Apr 2026] Title:ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller Authors:Haoxin Lin, Junjie Zhou, Daheng Xu, Yang Yu View a PDF of the paper titled ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller, by Haoxin Lin and 3 other authors View PDF HTML (experimental) 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. Subjects: Robotics (cs.RO); Machine Learning (cs.LG); Systems and Control (eess.SY) Cite as: arXiv:2604.04401 [cs.RO]   (or ar...