Automated driving systems require monitoring mechanisms to ensure safe operation, especially if system components degrade or fail. Their runtime self-representation plays a key role as it provides a-priori knowledge about the system's capabilities and limitations. In this paper, we propose a data-driven approach for deriving such a self-representation model for the motion controller of an automated vehicle. A conformalized prediction model is learned and allows estimating how operational conditions as well as potential degradations and failures of the vehicle's actuators impact motion control performance. During runtime behavior generation, our predictor can provide a heuristic for determining the admissible action space.

翻译：自动驾驶系统需要监控机制来确保安全运行，尤其是在系统组件退化或发生故障时。其运行时自我表征发挥着关键作用，因为它提供了关于系统能力与局限性的先验知识。本文提出了一种数据驱动方法，用于为自动驾驶车辆的运动控制器建立此类自我表征模型。通过训练共形化预测模型，能够估计运行条件以及车辆执行器潜在退化与故障对运动控制性能的影响。在运行时行为生成过程中，我们的预测器可为确定可行动作空间提供启发式策略。