Current issue
Online first
Archive
About the Journal
Aims and scope
Publisher and Editorial
Advertising policy
For Authors
Paper review procedures
Procedures protecting authentic authorship of papers
Paper preparation manual
Plagiarism check
Publication ethics
Reviewers
APC
Editorial and Scientific Board
Contact
Reviewers
Problems related to the operation of autonomous vehicles in adverse weather conditions
1
Departament od combustion engines and vehicles, University of Bielsko-Biała, Poland
Submission date: 2023-05-23
Final revision date: 2023-06-23
Acceptance date: 2023-06-26
Online publication date: 2023-07-22
Publication date: 2023-09-15
Corresponding author
Michał Brzozowski
Departament od combustion engines and vehicles, University of Bielsko-Biała, Willowa 2, 43-309, Bielsko-Biala, Poland
Departament od combustion engines and vehicles, University of Bielsko-Biała, Willowa 2, 43-309, Bielsko-Biala, Poland
Combustion Engines 2023,194(3), 109-115
KEYWORDS
TOPICS
ABSTRACT
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 measurments. 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.
REFERENCES (41)
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.20....
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.matc....
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.20....
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.2....
6.
Brummelen J, O’Brien M, Gruyer D, Najjaran H. Autono-mous vehicle perception: the technology of today and tomorrow, transport. Res. C-Emer. 2018;89:384-40. https://doi.org/10.1016/j.trc.....
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-....
8.
Brzozowski M. Autonomous cars, environmental sensors and problems of perception. Publisher: Wydawnictwo Naukowe Akademii Techniczno-Humanistycznej w Bielsku-Białej. Bielsko-Biala 2023 (in Polish).
9.
Colomb M, Duthon P, Laukkanen S. Characteristics of adverse weather conditions. DENSE 2021. https://www.dense247.eu/filead... (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/p....
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/rs1216....
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.meas....
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.2....
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.imav....
16.
Hadj-Bachir M, de Souza P. LIDAR sensor simulation in adverse weather condition for driving assistance develop-ment. 2019. https://hal.archives-ouvertes.... (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....
18.
Hespel L, Riviere N, Huet T, Tanguy B, Ceolato R. Perfor-mance evaluation of laser scanners through the atmosphere with adverse condition. The International Society for Optical Engineering. 2011. https://doi.org/10.1117/12.898....
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/app911....
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.2....
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.2....
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.ymss....
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....
24.
Maurer M, Gerdes J, Lenz B, Winner H. Autonomous driving technical, legal and social aspects. Publisher Springer Open 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.trpr....
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-20....
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.ifac....
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....
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-....
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....
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.2....
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.comc....
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.20....
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.ifac....
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....
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.iats....
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.2....
41.
Zimmermann M, Wotawa F. An adaptive system for autonomous driving. Software Qual J. 2020;28:1189-1212. https://doi.org/10.1007/s11219....
CITATIONS (3):
1.
Image-to-image machine translation enables computational defogging in real-world images
Anton Pollak, Rajesh Menon
Optics Express
Anton Pollak, Rajesh Menon
Optics Express
2.
ClearSight: A Unified Model to Enhance Traffic Sign Visibility Under Adverse Weather Conditions
Shanon Gunawardana, Geethma Raviprabha, Sasini Jayawardhana, Tharindu Weerakoon, Upul Jayasinghe
2024 Moratuwa Engineering Research Conference (MERCon)
Shanon Gunawardana, Geethma Raviprabha, Sasini Jayawardhana, Tharindu Weerakoon, Upul Jayasinghe
2024 Moratuwa Engineering Research Conference (MERCon)
3.
Enhancing Availability of Autonomous Shuttle Services: A Conceptual
Approach toward Challenges and Opportunities
Ali Dehghani, Hamza Salaar, Shanmuga Priya Srinivasan, Lixian Zhou, Georg Arbeiter, Alisa Lindner, Lucila Patino-Studencki
SAE International Journal of Connected and Automated Vehicles
Ali Dehghani, Hamza Salaar, Shanmuga Priya Srinivasan, Lixian Zhou, Georg Arbeiter, Alisa Lindner, Lucila Patino-Studencki
SAE International Journal of Connected and Automated Vehicles
Share
RELATED ARTICLE
| eISSN: | 2658-1442 |
| ISSN: | 2300-9896 |
The scientific journal is financed under the Agreement 185/WCN/2019/1 from the funds of the Minister of Science and Higher Education
© 2006-2024 Journal hosting platform by Bentus
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
