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Article Dans Une Revue Atmosphere Année : 2021

A quantitative analysis of point clouds from automotive lidars exposed to artificial rain and fog

Résumé

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.
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Dates et versions

hal-03258981 , version 1 (12-06-2021)

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Karl Montalban, Christophe Reymann, Dinesh Atchuthan, Paul-Edouard Dupouy, Nicolas Riviere, et al.. A quantitative analysis of point clouds from automotive lidars exposed to artificial rain and fog. Atmosphere, 2021, 12 (6), pp.738. ⟨10.3390/atmos12060738⟩. ⟨hal-03258981⟩
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