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

Research output: Contribution to journalArticleScientificpeer-review

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 languageEnglish
Article number2341
JournalApplied Sciences (Switzerland)
Volume9
Issue number11
DOIs
Publication statusPublished - 7 Jun 2019
MoE publication typeA1 Journal article-refereed

Fingerprint

weather
sensors
Sensors
Testing
Fog
Snow
fog
Railroad cars
snow
Scanning
scanning

Keywords

  • Adverse weather
  • Automatic driving
  • LiDAR

Cite this

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title = "Testing and validation of automotive point-cloud sensors in adverse weather conditions",
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.",
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author = "Maria Jokela and Matti Kutila and Pasi Pyyk{\"o}nen",
year = "2019",
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language = "English",
volume = "9",
journal = "Applied Sciences",
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Testing and validation of automotive point-cloud sensors in adverse weather conditions. / Jokela, Maria; Kutila, Matti; Pyykönen, Pasi.

In: Applied Sciences (Switzerland), Vol. 9, No. 11, 2341, 07.06.2019.

Research output: Contribution to journalArticleScientificpeer-review

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AU - Kutila, Matti

AU - Pyykönen, Pasi

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AB - 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.

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