Testing and validation of automotive point-cloud sensors in adverse weather conditions

Maria Jokela; Matti Kutila; Pasi Pyykönen

doi:10.3390/app9112341

Testing and validation of automotive point-cloud sensors in adverse weather conditions

Maria Jokela^*, Matti Kutila, Pasi Pyykönen

^*Corresponding author for this work

Research output: Contribution to journal › Article › Scientific › peer-review

102 Citations (Scopus)

Abstract

Light detection and ranging sensors (LiDARS) are the most promising devices for range sensing in automated cars and therefore, have been under intensive development for the last five years. Even though various types of resolutions and scanning principles have been proposed, adverse weather conditions are still challenging for optical sensing principles. This paper investigates proposed methods in the literature and adopts a common validation method to perform both indoor and outdoor tests to examine how fog and snow affect performances of different LiDARs. As suspected, the performance degraded with all tested sensors, but their behavior was not identical.

Original language	English
Article number	2341
Number of pages	14
Journal	Applied Sciences
Volume	9
Issue number	11
DOIs	https://doi.org/10.3390/app9112341
Publication status	Published - 7 Jun 2019
MoE publication type	A1 Journal article-refereed

Funding

This research was funded by the DENSE-EU-ECSEL project (Grant Agreement ID: 692449). DENSE is a joint European project which is sponsored by the European Commission under a joint undertaking.

Keywords

Adverse weather
Automatic driving
LiDAR

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10.3390/app9112341

Cite this

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Testing and validation of automotive point-cloud sensors in adverse weather conditions

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Keywords

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