Evaluating the performance of perception modules in autonomous driving is one of the most critical tasks in developing the complex intelligent system. While module-level unit test metrics adopted from traditional computer vision tasks are feasible to some extent, it remains far less explored to measure the impact of perceptual noise on the driving quality of autonomous vehicles in a consistent and holistic manner. In this work, we propose a principled framework that provides a coherent and systematic understanding of the impact an error in the perception module imposes on an autonomous agent's planning that actually controls the vehicle. Specifically, the planning process is formulated as expected utility maximisation, where all input signals from upstream modules jointly provide a world state description, and the planner strives for the optimal action by maximising the expected utility determined by both world states and actions. We show that, under practical conditions, the objective function can be represented as an inner product between the world state description and the utility function in a Hilbert space. This geometric interpretation enables a novel way to analyse the impact of noise in world state estimation on planning and leads to a universal metric for evaluating perception. The whole framework resembles the idea of transcendental idealism in the classical philosophical literature, which gives the name to our approach.

翻译：评估自动驾驶系统中感知模块的性能是开发这一复杂智能系统的关键任务之一。尽管沿用传统计算机视觉任务的模块级单元测试指标在一定程度上可行，但如何以一致且整体性的方式衡量感知噪声对自动驾驶车辆行驶质量的影响，仍远未得到充分探索。本文提出一种具有原理性的框架，系统连贯地揭示了感知模块中的误差如何影响实际控制车辆的自动驾驶智能体的规划过程。具体而言，我们将规划过程建模为期望效用最大化问题：上游模块提供的所有输入信号共同构成世界状态描述，规划者通过最大化由世界状态和行动共同决定的期望效用，寻求最优行动。我们证明，在实际条件下，目标函数可表示为希尔伯特空间中世界状态描述与效用函数的内积。这一几何解释为分析世界状态估计中的噪声对规划的影响提供了新视角，并衍生出衡量感知性能的通用指标。本框架在形式上与古典哲学文献中的“先验唯心论”思想相呼应——这正是本方法命名的由来。