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

TL;DR

ReinVBC is a model-based offline reinforcement learning method designed to improve vehicle braking control, reducing manual calibration and potentially replacing traditional anti-lock braking systems.

Contribution

The paper introduces ReinVBC, a novel offline model-based reinforcement learning approach tailored for vehicle braking control, with engineering enhancements for reliable dynamics modeling.

Findings

Demonstrates real-world vehicle braking performance

Shows potential to replace traditional anti-lock braking systems

Provides a reliable vehicle dynamics model

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.