Problems related to the operation of autonomous vehicles in adverse weather conditions

• Autorzy: Brzozowski Michał , Parczewski Krzysztof

Identyfikatory

DOI

10.19206/CE-168805

• Języki publikacji: EN

Abstrakty

EN

The article introduces and discusses the sensors used in autonomous cars. The reliability of these devices is crucial for the proper operation of autonomous driving systems. The research works related to the issue of the performance of autonomous sensors in adverse weather conditions is discussed and critically analysed. The negative effects caused by bad weather conditions are characterised. The paper presents the result of author's own research concern on the effects of rain, snow and fog on lidar measurements. The results obtained are presented, detailing the most important threats from each weather phenomenon. Attempts currently being made to address these issues are presented as well. The paper concludes with a summary of the research results, the current state of knowledge and suggestions for future developments.

Słowa kluczowe

EN

lidar; autonomous vehicle; adverse weather; rain; detection

PL

lidar; pojazd autonomiczny; niesprzyjające warunki pogodowe; deszcz; wykrywanie

• Wydawca: Polskie Towarzystwo Naukowe Silników Spalinowych
• Czasopismo: Combustion Engines
• Rocznik: 2023
• Tom: R. 62, nr 3
• Strony: 109--115
• Opis fizyczny: Bibliogr. 41 poz., il. kolor., 1 fot., 1 wykr.

Twórcy

autor

Brzozowski Michał

• Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biała, Poland

• https://orcid.org/0000-0001-9272-6594

autor

Parczewski Krzysztof

• Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biała, Poland

• https://orcid.org/0000-0002-2323-5078

Bibliografia

• [1] Aldibaja M, Suganuma N, Yoneda K. Improving localization accuracy for autonomous driving in snow-rain environments, International Symposium on System Integration. 2016:212-217. https://doi.org/10.1109/SII.2016.7844000
• [2] Anderson J, Kalra N, Stanley K, Sorensen P, Samaras C, Oluwatola T. Autonomous vehicle technology a guide for policymakers. Publisher Rand Corporation 2014.
• [3] Bertolazzi E, Frego M, Biral F. Point data reconstruction and smoothing using cubic splines and clusterization. Math Comput Simulat. 2020;176:36-56. https://doi.org/10.1016/j.matcom.2020.04.002
• [4] Bijelic M, Gruber T, Ritter W. Benchmarking image sensors under adverse weather conditions for autonomous driving. IEEE Int Veh Sym. 2018:1773-1779. https://doi.org/10.1109/IVS.2018.8500659
• [5] Blin R, Ainouz S, Canu S, Meriaudeau F. Road scenes analysis in adverse weather conditions by polarization-encoded images and adapted deep learning, IEEE Int C Intell Tr. 2019:27-32. https://doi.org/10.1109/ITSC.2019.8916853
• [6] Brummelen J, O’Brien M, Gruyer D, Najjaran H. Autonomous vehicle perception: the technology of today and tomorrow, transport. Res. C-Emer. 2018;89:384-40. https://doi.org/10.1016/j.trc.2018.02.012
• [7] Brzozowski M, Parczewski K. Validating adverse weather influence on LiDAR with an outdoor rain simulator. TRANSBALTICA XIII: Transportation Science and Technology. 2023:12-22. https://doi.org/10.1007/978-3-031-25863-3
• [8] Brzozowski M. Autonomous cars, environmental sensors and problems of perception. Publisher House in ATH Bielsko-Biała. Bielsko-Biala 2023 (in Polish).
• [9] Colomb M, Duthon P, Laukkanen S. Characteristics of adverse weather conditions. DENSE 2021. https://www.dense247.eu/fileadmin/user_upload/PDF/DENSE_D2.1_Characteristics_of_Adverse_Weather_Conditions.pdf (accessed on 2023.04.07)
• [10] Druml N, Maksymova I, Thurner T, Lierop D, Hannecke M, Foroutan A. 1D MEMS micro-scanning Lidar. International Conference on Sensor Device Technologies and Applications, Italy. 2018. https://www.researchgate.net/publication/326632441_1D_MEMS_MicroScanning_Lidar#fullTextFileContent
• [11] Farahnakian F, Heikkonen J. Deep learning based multi-modal fusion architectures for maritime vessel detection. Rem Sens. 2020:2509. https://doi.org/10.3390/rs12162509
• [12] Ferreira J, Martins A, Monteiro V. Intelligent transport systems, from research and development to the market uptake, Challenges in Object Detection Under Rainy Weather Conditions. Publisher Springer 2018.
• [13] Filgueira A, González-Jorge H, Lagüela S, Díaz-Vilariño L, Arias P. Quantifying the influence of rain in Lidar performance. Measurement. 2017;95:143-148. https://doi.org/10.1016/j.measurement.2016.10.009
• [14] Fu X, Huang J, Zeng D, Huang Y, Ding X, Paisley J. Removing rain from single images via a deep detail network. Proc Cvpr IEEE. 2017:3855-3863. https://doi.org/10.1109/CVPR.2017.186
• [15] Gehriga S, Schneidera N, Stalderb R, Franke U. Stereo vision during adverse weather - using priors to increase robustness in real-time stereo vision. Image Vision Comput. 2017;68:28-39. https://doi.org/10.1016/j.imavis.2017.07.008
• [16] Hadj-Bachir M, de Souza P. LIDAR sensor simulation in adverse weather condition for driving assistance development. 2019. https://hal.archives-ouvertes.fr/hal-01998668/document (accesed on 2023.04.17).
• [17] Hahner M, Sakaridis C, Bijelic M, Heide F, Yu F, Dai D et al. LiDAR snowfall simulation for robust 3D object detection, CVF. arXiv. 2203:15118. https://doi.org/10.48550/arXiv.2203.15118
• [18] Hespel L, Riviere N, Huet T, Tanguy B, Ceolato R. Performance evaluation of laser scanners through the atmosphere with adverse condition. The International Society for Optical Engineering. 2011. https://doi.org/10.1117/12.898010
• [19] Jokela M, Kutila M, Pyykönen P. Testing and validation of automotive point-cloud sensors in adverse weather conditions, Appl. Sci. 2019;9:2341. https://doi.org/10.3390/app9112341
• [20] Kutila M, Pyykonen P, Holzhuter H, Colomb M, Duthon P. Automotive Lidar performance verification in fog and rain. IIEEE Int C Intell Tr. 2018:1695-1701. https://doi.org/10.1109/ITSC.2018.8569624
• [21] Kutila M, Pyykönen P, Ritter W, Sawade O, Schaufele B. Automotive LIDAR sensor development scenarios for harsh weather conditions. IIEEE Int C Intell Tr. 2016:265-270. https://doi.org/10.1109/ITSC.2016.7795565
• [22] Li S, Li G, Yu J, Liu C, Cheng B, Wang J et al. Kalman filter-based tracking of moving objects using linear ultrasonic sensor array for road vehicles. Mech Syst Signal Pr. 2018;98:173-189. https://doi.org/10.1016/j.ymssp.2017.04.041
• [23] Liu M-Y, Breuel T, Kautz J. Unsupervised image-to-image translation networks. Adv Neur In. 2017:700-708. https://doi.org/10.48550/arXiv.1703.00848
• [24] Maurer M, Gerdes J, Lenz B, Winner H. Autonomous driving technical, legal and social aspects. Publisher Springer 2016.
• [25] Ondruš J, Kolla E, Vertaľ P, Šarić Z. How do autonomous cars work? Transp Res Proc. 2020;44:226-233. https://doi.org/10.1016/j.trpro.2020.02.049
• [26] Parczewski K, Romaniszyn K, Wnęk H. Influence of electric motors assembly in hubs of vehicle wheels on the dynamics of movement, especially on surfaces with different adhesion coefficient. Combustion Engines. 2019;179(4):58-64. https://doi.org/10.19206/CE-2019-409
• [27] Porębski J, Kogut K, Markiewicz P, Skruch P. Occupancy grid for static environment perception in series automotive applications, IFAC PapersOnLine. 2019;52:148-153. https://doi.org/10.1016/j.ifacol.2019.08.063
• [28] Qian R, Tan RT, Yang W, Su J, Liu J. Attentive generative adversarial network for raindrop removal from a single image. Proc Cvpr IEEE. 2018:2482-2491. https://doi.org/10.48550/arXiv.1711.10098
• [29] Rasshofer RH, Spies M, Spies H. Influences of weather phenomena on automotive laser radar systems. Adv Radio Sci. 2011;9:49-60. https://doi.org/10.5194/ars-9-49-2011
• [30] Ren D, Zuo W, Hu Q, Zhu P, Meng D. Progressive image deraining networks: a better and simpler baseline. Proceedings of the Proc Cvpr IEEE. 2019:3937-3946. https://doi.org/10.48550/arXiv.1901.09221
• [31] SAE International. 2021. Last accessed: 2023-05-08. https://www.sae.org/news/press-room/2018/12/sae-international-releases-updated-visual-chart-for-its-%E2%80%9Clevels-of-driving-automation%E2%80%9D-standard-for-self-driving-vehicles
• [32] Schechner Y, Narasimhan S, Nayar S. Instant dehazing of images using polarization. Proc Cvpr IEEE. 2001;1:325-332. https://doi.org/10.1109/CVPR.2001.990493
• [33] Shit R. Precise localization for achieving next-generation autonomous navigation: state-of-the-art, taxonomy and future prospects. Computer Communications. 2020;160:351-374. https://doi.org/10.1016/j.comcom.2020.06.007
• [34] Sjafire H. Introduction to self-driving vehicle technology. Publisher Chapman and Hall 2020.
• [35] Slutsky M, Dobkin D. Dual inverse sensor model for radar occupancy grids. IEEE Int Veh Sym. 2019:1550-1557. https://doi.org/10.1109/IVS.2019.8813772
• [36] Wang X, Wang W, Yin X, Xiang C, Zhang Y. A new grid map construction method for autonomous vehicles. IFAC PapersOnLine. 2018:377-382. https://doi.org/10.1016/j.ifacol.2018.10.077
• [37] Wojtanowski J, Zygmunt M, Kaszczuk M, Mierczyk Z, Muzal M. Comparison of 905 nm and 1550 nm semiconductor laser rangefinders’ performance deterioration due to adverse environmental conditions. Opto-Electron Rev. 2014;22(3): 183-190. https://doi.org/10.2478/s11772-014-0190-2
• [38] Yoneda K, Suganuma N, Yanase R, Aldibaja M. Automated driving recognition technologies for adverse weather conditions. IATSS Research. 2019;43:253-262. https://doi.org/10.1016/j.iatssr.2019.11.005
• [39] Zang S, Ding M. The impact of adverse weather conditions on autonomous vehicles: examining how rain, snow, fog, and hail affect the performance of a self-driving car. Vehicular Technology Magazine. 2019;99:1. https://doi.org/10.1109/MVT.2019.2892497
• [40] Zhang C, Ang MH, Rus D. Robust LIDAR localization for autonomous driving in rain. IEEE Int C Int Robot. 2018:3409-3415. https://doi.org/10.1109/IROS.2018.8593703
• [41] Zimmermann M, Wotawa F. An adaptive system for autonomous driving. Software Qual J. 2020;28:1189-1212. https://doi.org/10.1007/s11219-020-09519-w

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).