Evidence map›Paper›PMID 40218705›Full record

ArticleSensors (Basel, Switzerland)2025

Effects of Dust and Moisture Surface Contaminants on Automotive Radar Sensor Frequencies.

Jeongmin Kang, Oskar Hamidi, Karl Vanäs, Tobias Eidevåg, Emil Nilsson, Ross Friel

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jeongmin KangSchool of Information Technology, Halmstad University, 30118 Halmstad, Sweden.ORCID 0000-0001-6110-1428
Oskar HamidiVolvo Car Corporation, 41878 Gothenburg, Sweden.
Karl VanäsVolvo Car Corporation, 41878 Gothenburg, Sweden.
Tobias EidevågVolvo Car Corporation, 41878 Gothenburg, Sweden.ORCID 0000-0003-1537-2133
Emil NilssonSchool of Information Technology, Halmstad University, 30118 Halmstad, Sweden.ORCID 0000-0003-2783-7460
Ross FrielSchool of Information Technology, Halmstad University, 30118 Halmstad, Sweden.ORCID 0000-0002-0480-4079

Funding

Sweden's Innovation Agency (VINNOVA) 2023-02609
6 · The paper itself

Abstract

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.

Indexed as

autonomous vehiclesobject detectionradarsurface contamination

Identifiers

PMID40218705
PMCPMC11991060

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.