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Effects of Dust and Moisture Surface Contaminants on Automotive Radar Sensor Frequencies
School of Information Technology, Halmstad University, 30118 Halmstad, Sweden.ORCID iD: 0000-0001-6110-1428
Volvo Car Corporation, 41878 Gothenburg, Sweden.
Volvo Car Corporation, 41878 Gothenburg, Sweden.
Volvo Car Corporation, 41878 Gothenburg, Sweden.ORCID iD: 0000-0003-1537-2133
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2025 (English)In: Sensors, E-ISSN 1424-8220, E-ISSN 1424-8220, Vol. 25, no 7, p. 2192-2192Article in journal (Refereed) Published
Abstract [en]

Perception and sensing of the surrounding environment are crucial for ensuring the safety of autonomous driving systems. A key issue is securing sensor reliability from sensors mounted on the vehicle and obtaining accurate raw data. Surface contamination in front of a sensor typically occurs due to adverse weather conditions or particulate matter on the road, which can degrade system reliability depending on sensor placement and surrounding bodywork geometry. Moreover, the moisture content of dust contaminants can cause surface adherence, making it more likely to persist on a vertical sensor surface compared to moisture only. In this work, a 76–81 GHz radar sensor, a 72–82 GHz automotive radome tester, a 60–90 GHz vector network analyzer system, and a 76–81 GHz radar target simulator setup were used in combination with a representative polypropylene plate that was purposefully contaminated with a varying range of water and ISO standard dust combinations; this was used to determine any signal attenuation and subsequent impact on target detection. The results show that the water content in dust contaminants significantly affects radar signal transmission and object detection performance, with higher water content levels causing increased signal attenuation, impacting detection capability across all tested scenarios.

Place, publisher, year, edition, pages
2025. Vol. 25, no 7, p. 2192-2192
Keywords [en]
autonomous vehicles; radar; surface contamination; object detection
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-55913DOI: 10.3390/s25072192OAI: oai:DiVA.org:hh-55913DiVA, id: diva2:1954028
Part of project
Quantifying Sensor Surface Contamination for Safe Vehicle Automation
Funder
Vinnova, 2023-02609Available from: 2025-04-23 Created: 2025-04-23 Last updated: 2025-04-23

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Publisher's full texthttps://www.mdpi.com/1424-8220/25/7/2192

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Nilsson, EmilFriel, Ross

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