ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller
This work addresses the need for more efficient and automated braking system calibration in the vehicle industry, though it appears incremental as it builds on existing model-based reinforcement learning methods with engineering designs.
The authors tackled the problem of reducing manual calibration in vehicle braking controllers by applying an offline model-based reinforcement learning approach, achieving results that demonstrate the method's capability in real-world braking and potential to replace production-grade anti-lock braking systems.
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.