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

2026-04-06

AI Summary

This work proposes an offline model-based reinforcement learning approach that integrates engineering priors to address the inefficiency of traditional vehicle braking controllers, which rely heavily on manual calibration. By leveraging data-driven techniques, the method constructs a high-fidelity vehicle dynamics model and subsequently optimizes braking strategies without requiring online interaction with the environment. The resulting controller achieves braking performance comparable to production-grade anti-lock braking systems (ABS) in real-world braking tasks, demonstrating significant potential as a viable alternative to current industry-standard systems.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Behavior Learning & Control

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Vehicle Braking Controller
• Offline Reinforcement Learning
• Model-based Reinforcement Learning
• Braking System Calibration
• Anti-lock Braking System

Innovation

• model-based reinforcement learning
• offline reinforcement learning
• vehicle braking control
• data-driven dynamics model
• anti-lock braking system