Assessing the Impact of Fog on Autonomous Vehicle Perception Systems
Resumo
This study addresses the impact of adverse weather, specifically fog, on the perception systems of autonomous vehicles, which are critical for detecting and responding to traffic scenarios. Using over 10,000 images, an object recognition model was developed with Roboflow and YOLOv8, while fog disturbances were generated with GANs. The research simulates various traffic scenarios, comparing system performance under clear and foggy conditions. Results show that training models with a wider range of conditions enhances accuracy, highlighting the importance of diverse training for safe autonomous vehicle operation. This work offers insights for improving perception systems in autonomous vehicles.
Referências
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Mark Everingham, Luc Van Gool, Christopher K.I. Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88(2):303–338, 6 2010. ISSN 09205691. DOI: 10.1007/S11263-009-0275-4/METRICS. URL [link].
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Jinlong Li, Runsheng Xu, Jin Ma, Qin Zou, Jiaqi Ma, and Hongkai Yu. Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather. URL [link].
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng Yang Fu, and Alexander C. Berg. SSD: Single shot multibox detector. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9905 LNCS:21–37, 2016. ISSN 16113349. DOI: 10.1007/978-3-319-46448-0_2/FIGURES/5. URL [link].
Zhaohui Liu, Shiji Zhao, and Xiao Wang. Research on Driving Obstacle Detection Technology in Foggy Weather Based on GCANet and Feature Fusion Training. Sensors 2023, Vol. 23, Page 2822, 23 (5):2822, 3 2023. ISSN 1424-8220. DOI: 10.3390/S23052822. URL [link].
Xianglin Meng, Yi Liu, Lili Fan, and Jingjing Fan. YOLOv5s-Fog: An Improved Model Based on YOLOv5s for Object Detection in Foggy Weather Scenarios. Sensors 2023, Vol. 23, Page 5321, 23(11):5321, 6 2023. ISSN 1424-8220. DOI: 10.3390/S23115321. URL [link].
Yi Lu Murphey, Ilya Kolmanovsky, and Paul Watta. Advances, Opportunities and Challenges in AI-enabled Technologies for Autonomous and Connected Vehicles. Lecture Notes in Intelligent Transportation and Infrastructure, Part F1376:1–16, 2023. ISSN 2523-3459. DOI: 10.1007/978-3-031-06780-8_1. URL [link].
Noor Ul Ain Tahir, Zuping Zhang, Muhammad Asim, Junhong Chen, and Mohammed ELAffendi. Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches. Algorithms 2024, Vol. 17, Page 103, 17 (3):103, 2 2024. ISSN 1999-4893. DOI: 10.3390/A17030103. URL [link].
Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, and Trevor Darrell. BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling. 5 2018. URL [link].
Emmanuel Owusu Appiah and Solomon Mensah. Object detection in adverse weather condition for autonomous vehicles. Multimedia Tools and Applications, 83(9):28235–28261, 3 2024. ISSN 15737721. DOI: 10.1007/S11042-023-16453-Z/METRICS. URL [link].
Pedro Azevedo. BDD100k Dataset. Roboflow Universe, 2023.
B. Dwyer, J. Nelson, T. Hansen, and et al. Roboflow (Version 1.0), 2024.
Mark Everingham, Luc Van Gool, Christopher K.I. Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88(2):303–338, 6 2010. ISSN 09205691. DOI: 10.1007/S11263-009-0275-4/METRICS. URL [link].
Caner Filiz and Caner Filiz. Can Autonomous Vehicles Prevent Traffic Accidents? Accident Analysis and Prevention, 6 2020. DOI: 10.5772/INTECHOPEN.93020. URL [link].
André Hartwecker, Steffen Müller, and Christian Schyr. Safety of Use Analysis for Autonomous Driving Functions Under Laboratory Conditions. Lecture Notes in Mechanical Engineering, pages 1183–1192, 2022. ISSN 2195-4364. DOI: 10.1007/978-3-031-07305-2_110. URL [link].
Philip Koopman, Uma Ferrell, Frank Fratrik, and Michael Wagner. A Safety Standard Approach for Fully Autonomous Vehicles. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11699 LNCS:326–332, 2019. ISSN 1611-3349. DOI: 10.1007/978-3-030-26250-1_26. URL [link].
Jinlong Li, Runsheng Xu, Jin Ma, Qin Zou, Jiaqi Ma, and Hongkai Yu. Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather. URL [link].
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng Yang Fu, and Alexander C. Berg. SSD: Single shot multibox detector. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9905 LNCS:21–37, 2016. ISSN 16113349. DOI: 10.1007/978-3-319-46448-0_2/FIGURES/5. URL [link].
Zhaohui Liu, Shiji Zhao, and Xiao Wang. Research on Driving Obstacle Detection Technology in Foggy Weather Based on GCANet and Feature Fusion Training. Sensors 2023, Vol. 23, Page 2822, 23 (5):2822, 3 2023. ISSN 1424-8220. DOI: 10.3390/S23052822. URL [link].
Xianglin Meng, Yi Liu, Lili Fan, and Jingjing Fan. YOLOv5s-Fog: An Improved Model Based on YOLOv5s for Object Detection in Foggy Weather Scenarios. Sensors 2023, Vol. 23, Page 5321, 23(11):5321, 6 2023. ISSN 1424-8220. DOI: 10.3390/S23115321. URL [link].
Yi Lu Murphey, Ilya Kolmanovsky, and Paul Watta. Advances, Opportunities and Challenges in AI-enabled Technologies for Autonomous and Connected Vehicles. Lecture Notes in Intelligent Transportation and Infrastructure, Part F1376:1–16, 2023. ISSN 2523-3459. DOI: 10.1007/978-3-031-06780-8_1. URL [link].
Noor Ul Ain Tahir, Zuping Zhang, Muhammad Asim, Junhong Chen, and Mohammed ELAffendi. Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches. Algorithms 2024, Vol. 17, Page 103, 17 (3):103, 2 2024. ISSN 1999-4893. DOI: 10.3390/A17030103. URL [link].
Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, and Trevor Darrell. BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling. 5 2018. URL [link].
Publicado
28/11/2024
Como Citar
SILVA, Alexandre Ray da; BATISTA CAMARGO JÚNIOR, João.
Assessing the Impact of Fog on Autonomous Vehicle Perception Systems. In: WORKSHOP LATINOAMERICANO DE DEPENDABILIDADE E SEGURANÇA EM SISTEMAS VEICULARES, 1. , 2024, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 9-12.
DOI: https://doi.org/10.5753/ssv.2024.32621.
