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.
翻译:在自主驾驶的情况下,车辆必然会遇到更极端的天气,在这种天气下,必须保证公共安全。随着气候迅速变化,预计大雪暴的频率会增加,成为安全航行的一个主要威胁。虽然有许多文献旨在改善航行对冬季条件的适应能力,但缺乏标准指标来量化降水相关利达尔传感器的可见度损失。本章根据气象学研究领域的可见度概念,提出了一个新的指标,以实时量化利达尔可见度损失。我们评估了加拿大不利驾驶条件(CADC)数据集的这一指标,将其与以利达尔为基础的最新本地化算法的性能相联系,并评估了在本地化进程之前过滤点云的好处。我们表明,隐性近地点(ICP)算法对降雪的冲击是惊人的,但突发事件(如雪螺)会大大妨碍其准确性。我们讨论这些事件,并表明需要更精确地将数据放在这些极端事件上,以量化其影响。