用残差强化学习自动校准汽车控制器，省时省力还可解释。
Production-Ready Automated ECU Calibration using Residual Reinforcement Learning
- 基于残差强化学习，从初始地图快速优化控制参数。
- 在硬件在环平台测试中，校准结果接近量产控制器基准。
- 适合汽车厂商用于高效、可解释的自动化标定流程。
电子控制单元（ECU）在现代汽车中起着核心作用，其控制功能的行为高度依赖于校准参数，传统上由工程师手动设计。然而，随着客户期望提升、产品开发周期缩短以及排放法规日益严格，面对众多车型变体，人工校准已难以为继。已有研究证明强化学习（RL）可自动生成最优控制函数，但因其基于神经网络，缺乏可解释性，难以用于生产。本文提出一种基于残差强化学习的可解释自动化校准方法，遵循汽车开发规范。通过在硬件在环（HiL）平台上对系列ECU中的映射式空气路径控制器进行验证，从次优初始映射出发，该方法迅速收敛至接近量产基准的校准结果。实验表明，该方法在工业场景中可显著缩短时间、减少人工干预并获得更优性能。
原文摘要 · Abstract (English)
Electronic Control Units (ECUs) have played a pivotal role in transforming motorcars of yore into the modern vehicles we see on our roads today. They actively regulate the actuation of individual components and thus determine the characteristics of the whole system. In this, the behavior of the control functions heavily depends on their calibration parameters which engineers traditionally design by hand. This is taking place in an environment of rising customer expectations and steadily shorter product development cycles. At the same time, legislative requirements are increasing while emission standards are getting stricter. Considering the number of vehicle variants on top of all that, the conventional method is losing its practical and financial viability. Prior work has already demonstrated that optimal control functions can be automatically developed with reinforcement learning (RL); since the resulting functions are represented by artificial neural networks, they lack explainability, a circumstance which renders them challenging to employ in production vehicles. In this article, we present an explainable approach to automating the calibration process using residual RL which follows established automotive development principles. Its applicability is demonstrated by means of a map-based air path controller in a series control unit using a hardware-in-the-loop (HiL) platform. Starting with a sub-optimal map, the proposed methodology quickly converges to a calibration which closely resembles the reference in the series ECU. The results prove that the approach is suitable for the industry where it leads to better calibrations in significantly less time and requires virtually no human intervention
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