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Evaluating the performance of perception modules in autonomous driving is oneof the most critical tasks in developing the complex intelligent system. Whilemodule-level unit test metrics adopted from traditional computer vision tasksare feasible to some extent, it remains far less explored to measure the impactof perceptual noise on the driving quality of autonomous vehicles in aconsistent and holistic manner. In this work, we propose a principled frameworkthat provides a coherent and systematic understanding of the impact an error inthe perception module imposes on an autonomous agent's planning that actuallycontrols the vehicle. Specifically, the planning process is formulated asexpected utility maximisation, where all input signals from upstream modulesjointly provide a world state description, and the planner strives for theoptimal action by maximising the expected utility determined by both worldstates and actions. We show that, under practical conditions, the objectivefunction can be represented as an inner product between the world statedescription and the utility function in a Hilbert space. This geometricinterpretation enables a novel way to analyse the impact of noise in worldstate estimation on planning and leads to a universal metric for evaluatingperception. The whole framework resembles the idea of transcendental idealismin the classical philosophical literature, which gives the name to ourapproach.

Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous Driving