TRANSCENDENTAL IDEALISM OF PLANNER: EVALUATING PERCEPTION FROM THE PLANNING PERSPECTIVE FOR AUTONOMOUS DRIVING Anonymous authors Paper under double-blind review

Abstract

Evaluating the performance of perception module in autonomous driving is one of the most critical tasks in developing these complex intelligent systems. While module-level unit test methodologies adopted from traditional computer vision tasks are viable to a certain extent, it still remains far less explored to evaluate how changes in a perception module can impact the planning of an autonomous vehicle in a consistent and holistic manner. In this work, we propose a principled framework that provides a coherent and systematic understanding of how perception modules affect the planning of an autonomous vehicle that actually controls the vehicle. Specifically, planning of an autonomous vehicle is formulated as an expected utility maximisation problem, where all input signals from upstream modules jointly provide a world state description, and the planner aims to find the optimal action to execute by finding the solution to maximise the expected utility determined by both the world state and the action. We show that, under some mild 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 the solution to the problem, and leads to a universal quantitative metric for such purpose. The whole framework resembles the idea of transcendental idealism in the classical philosophy literature, which gives the name to our approach.

1. INTRODUCTION

Autonomous driving has recently risen as a fast-advancing realm in both industry and academia, and receives a surge of interest from engineering and scientific communities (Yurtsever et al., 2020; Sun et al., 2020) . As an intricate system, an autonomous driving vehicle consists of numerous hardware components and interactive onboard modules. As one such core component, the onboard perception module serves as the major source of real-time characterisation of the dynamic environment an autonomous vehicle (AV) navigates through. To evaluate and improve the perception module, conventional perception tasks (such as detection, segmentation, tracking) have been well defined and corresponding performance measurements are established in computer vision to benchmark performance of perception algorithms (Lin et al., 2014) . Despite their great success in driving the development of advanced perceptual information processing modules, almost all such metrics exclusively focus on the perception-level performance in a deployment-agnostic fashion, for instance, how close a detected object is to the ground truth, while ignoring the actual impact of the result to the entire AV system. Indeed, not all perception errors translate the same to the planning of an AV. Obviously, miss detecting a vehicle in front of an AV is far more serious than one behind far away. This problem is further compounded by the heterogeneity of perception errors that share little semantics in common ("How dose an error of 5m/s in velocity compare to that of a size 25% larger?"), where intuitive manual engineering is widely used (Caesar et al., 2020) . Although these issues are typically addressed by integrating road test in the real world, the process is extremely costly and time-consuming (Wachenfeld and Winner, 2016; Åsljung et al., 2017) . In result, tools are in great demand to effectively and efficiently measure the impact of perception to the whole autonomous driving system before deployment on road. Unfortunately, these solutions still remain far less explored in the research literature. 1

