Abstract

The testing and safety cases of Assisted and Automated Driving functions require considerations for non ideal environmental conditions, such as adverse and extreme weather. In these extreme conditions, perception sensors (e.g. camera, LiDAR, RADAR), used to build the situational awareness of the vehicle, might produce noisy and degraded data, and it is therefore key to consider: (i) how to reliably and robustly measure data degradation; (ii) how to evaluate de-noising techniques. This paper focuses on de-snowing of LiDAR data, as falling snow is one of the most variable and dangerous conditions to be encounter while driving-and LiDAR can provide essential 3D information to still enable safe vehicle navigation. Using the WADS dataset, which contains segmented pointclouds including falling and deposited snow points, 4 different state-of-the-art desnowing techniques are compared using an array of adapted pointcloud quality metrics, and combined with perception based metrics. The different metrics are able to capture different aspects of the data degradation, and hereby the novel De-Snow Score (DSS) is proposed and applied to have a holistic evaluation of the de-noising techniques. Based on DSS, the most promising de-noising algorithms are identified. The proposed methodology and De-Snow Score can pave the way for a standardised approach when measuring perception sensor data degradation and de-noising.