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Title [sv]
Quantifying Sensor Surface Contamination for Safe Vehicle Automation
Title [en]
Quantifying Sensor Surface Contamination for Safe Vehicle Automation
Abstract [sv]
Syfte och mål:Det primära syftet med detta forskningsprojekt är att kvantifiera hur föroreningar på sensorytor påverkar signalprestandan hos exteriöra fordonssensorer.Specifika mål är:- Kontrollerade experiment i olika miljöer- Utvärdering av olika sensorer- Framsteg inom Shift-Left-testning- Förståelse för konsekvenser på sensorernas signalprestanda- Förbättring av säkerhet och tillförlitlighet för ADAS och ADFörväntade effekter och resultat:Sammantaget syftar detta projekt till att avsevärt förbättra funktionaliteten och tillförlitligheten hos AD- och ADAS-system i ett bredare spektrum av miljöförhållanden, vilket leder till en säkrare, mer inkluderande och hållbar vägtransport.Specifika effekter är:- Förbättrad fordonssuppfattning i varierande väder- Grund för att designa mer robusta system- Bättre förståelse för nuvarande uppfattningssystem- Mer hållbara lösningar för sensorrengöring- Minskad miljöpåverkan från olyckor- Ökad nyttjandegrad av fordon- Framsteg inom vetenskaplig forskningUpplägg och genomförande:Strukturen består av fyra arbetspaket:WP1 (Q1 2024 - Q1 2025): fokuserar på att studera försämring av sensorfunktion p g a föroreningar, i elektromagnetiskt dämpad kammare, samt integrera resultaten med Volvo-fordon.WP2 (Q2 2024 - Q4 2024): syftar till att mäta ytföroreningar i vindtunnlar, och utveckla dynamiska mätstrategier.WP3 (Q1 2025 - Q3 2025): testar sensorer i vindtunnlar, och förbereder för LiDAR-integration.WP4 (Q1, Q3 2024, Q4 2025): innefattar realtidstester av fordon för sensorns prestanda i ogynnsamt väder.
Abstract [en]
Purpose and goal:The primary aim of this research project is to quantify how sensor surface contamination affects the sensor signal performance of exterior vehicle sensors.Specific objectives are:- Controlled Experimentation in Diverse Environments- Evaluating Different Sensors- Advancing Shift-Left Testing- Understanding Sensor Signal Performance Consequences- Enhancing Safety and Reliability of ADAS and ADExpected results and effects:Overall, this project aims to significantly enhance the functionality and reliability of AD and ADAS systems across a wider range of environmental conditions, leading to safer, more inclusive, and sustainable road transport.Specific effects are: - Improved Vehicle Perception in Varying Weather- Foundation for Designing more Robust Systems- Better Understanding Current Perception Systems- More Sustainable Sensor Cleaning Solutions- Reduced Environmental Impact from Accidents:- Increased Vehicle Utilization- Advancement in Scientific ResearchApproach and implementation:The structure is four work packages: WP1 (Q1 2024 - Q1 2025): focuses on studying sensor degradation from contaminants in anechoic chambers, integrating the findings with Volvo Cars findings.WP2 (Q2 2024 - Q4 2024): aims to measure surface contamination in wind tunnels, developing dynamic measurement strategies.WP3 (Q1 2025 - Q3 2025): tests sensors in wind tunnels, preparing for LiDAR integration.WP4 (Q1, Q3 2024, Q4 2025): involves real-time vehicle tests for sensor performance in adverse weather.
Publications (1 of 1) Show all publications
Kang, J., Hamidi, O., Vanäs, K., Eidevåg, T., Nilsson, E. & Friel, R. (2025). Effects of Dust and Moisture Surface Contaminants on Automotive Radar Sensor Frequencies. Sensors, 25(7), 1-18, Article ID 2192.
Open this publication in new window or tab >>Effects of Dust and Moisture Surface Contaminants on Automotive Radar Sensor Frequencies
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2025 (English)In: Sensors, E-ISSN 1424-8220, Vol. 25, no 7, p. 1-18, article id 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. © 2025 by the authors.

Place, publisher, year, edition, pages
Basel: MDPI, 2025
Keywords
autonomous vehicles, radar, surface contamination, object detection
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-55913 (URN)10.3390/s25072192 (DOI)001465645300001 ()40218705 (PubMedID)2-s2.0-105002280369 (Scopus ID)
Funder
Vinnova, 2023-02609
Available from: 2025-04-23 Created: 2025-04-23 Last updated: 2025-10-01Bibliographically approved
Principal InvestigatorFriel, R. J.
Coordinating organisation
Halmstad University
Period
2023-11-01 - 2025-11-15
National Category
RoboticsSignal ProcessingOther Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
DiVA, id: project:3163Project, id: 2023-02609_Vinnova