Transcendental Idealism of Planner: Evaluating Perception from Planning
  Perspective for Autonomous Driving
Wei-Xin Li Xiaodong Yang
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.