Abstract
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.
Abstract (translated)
自动驾驶系统需要监控机制以确保安全运行，尤其是系统组件退化或失效时。其运行时自表示在确定系统能力与限制方面发挥着关键作用。在本文中，我们提出了一个数据驱动的方法，用于得出这样的自表示模型，该模型用于自动车辆运动控制器的运动控制器。学得了平滑预测模型，允许估计操作条件以及潜在的车辆执行器故障和运动控制性能的影响。在运行时行为生成期间，我们的预测器可以提供一种经验性的确定可允许动作空间的指导方针。
URL
https://arxiv.org/abs/2404.16500
