Abstract
Light Detection And Ranging sensors (lidar) are key to autonomous driving,
but their data is severely impacted by weather events (rain, fog, snow). To increase safety and availability of self-driving vehicles,
the analysis of the phenomena consequences at stake is necessary. This paper presents experiments performed in a climatic chamber with
lidars of different technologies (spinning, Risley prisms, micro-motion and MEMS), that are compared in various rain and fog artificial conditions.
A specific target with calibrated reflectance is used to make a first quantitative analysis. We observe different results depending on the sensors,
and unexpected behaviors in the analysis with artificial rain are seen where higher rain rates does not necessarily mean higher degradations on lidar data.
My Contributions to this work
This work was led Karl Montalban, PhD at EasyMile, in collaboration with LAAS-CNRS and ONERA-Toulouse.
I had the opportunity to assist Karl and Christophe Reymann, R&D Perception Enginner at EasyMile, and participate to scientific decisions.
In the long-term, my participation to this work will help me to better understand possible solutions we could propose to issues we are facing at EasyMile on
degraded weather subjects.