用强化学习自动优化刹车系统，减少人工调校。
ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller
- 基于离线模型的强化学习，利用数据构建车辆动力学模型。
- 在真实车辆上实现稳定刹车，性能媲美现有防抱死系统。
- 适合自动驾驶与智能底盘研发人员参考。
刹车系统是保障当前车辆安全性和操控性的关键模块，其生产过程依赖大量人工调校。降低人力与时间成本的同时保持车辆刹车控制器（VBC）性能，对汽车行业意义重大。本文提出 ReinVBC，采用离线模型基强化学习方法解决车辆刹车控制问题。通过引入有效的工程设计，提升动态模型的可靠性与刹车策略的执行能力。实验结果表明，该方法在真实车辆刹车场景中表现稳健，具备替代现有量产级防抱死刹车系统（ABS）的潜力。
原文摘要 · Abstract (English)
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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