悪天候下の自動運転における視界定量化の重要性
On the Importance of Quantifying Visibility for Autonomous Vehicles under Extreme Precipitation
降雪時のLiDAR視界損失を気象学の視程概念に基づきリアルタイムで定量化する新指標を提案し、CADCデータセットで評価した。ICPアルゴリズムは降雪に頑健だが突発的な吹雪には弱く、極端事象に焦点を当てたデータセットの必要性を示した。
著者: Clément Courcelle, Dominic Baril, François Pomerleau, Johann Laconte
分類: cs.RO
原文アブストラクト
In the context of autonomous driving, vehicles are inherently bound to encounter more extreme weather during which public safety must be ensured. As climate is quickly changing, the frequency of heavy snowstorms is expected to increase and become a major threat to safe navigation. While there is much literature aiming to improve navigation resiliency to winter conditions, there is a lack of standard metrics to quantify the loss of visibility of lidar sensors related to precipitation. This chapter proposes a novel metric to quantify the lidar visibility loss in real time, relying on the notion of visibility from the meteorology research field. We evaluate this metric on the Canadian Adverse Driving Conditions (CADC) dataset, correlate it with the performance of a state-of-the-art lidar-based localization algorithm, and evaluate the benefit of filtering point clouds before the localization process. We show that the Iterative Closest Point (ICP) algorithm is surprisingly robust against snowfalls, but abrupt events, such as snow gusts, can greatly hinder its accuracy. We discuss such events and demonstrate the need for better datasets focusing on these extreme events to quantify their effect.
