# On the Importance of Quantifying Visibility for Autonomous Vehicles under Extreme Precipitation

arXiv: https://arxiv.org/abs/2209.03226 · HTML: https://mlsanity.com/paper/2209.03226/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2022.

## Built on by (most cited first, 4 of 4)

- [Proprioception Is All You Need: Terrain Classification for Boreal Forests](https://mlsanity.com/paper/2403.16877/) (2024)
- [Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights](https://mlsanity.com/paper/2309.08731/) (2023)
- [Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm](https://mlsanity.com/paper/2403.05666/) (2024)
- [Beyond the Snowflakes: Disentangling Weather Effects from Confounding Meta-Factors in LiDAR Object Detection](https://mlsanity.com/paper/2610.13891/) (2026)

## Abstract (arXiv)

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.