Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving

Bijelic, Mario; Gruber, Tobias; Ritter, Walter

doi:10.1109/ivs.2018.8500659

Cited by 69 publications

(43 citation statements)

References 19 publications

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“…Performance of semantic segmentation on daytime scenes has increased rapidly in recent years. As a consequence, attention is now turning to adaptation to adverse conditions [23], [31], [32], [33]. A case in point are recent efforts to adapt clear-weather models to fog [10], [34], [35], [36], by using both labeled synthetic images and unlabeled real images of increasing fog density.…”

Section: Related Workmentioning

confidence: 99%

Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation

Sakaridis

Dai

Gool

2022

IEEE Trans. Pattern Anal. Mach. Intell.

102

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We address the problem of semantic nighttime image segmentation and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreover, we design a new evaluation framework to address the substantial uncertainty of semantics in nighttime images. Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night through progressively darker times of day, exploiting cross-time-of-day correspondences between daytime images from a reference map and dark images to guide the label inference in the dark domains; 2) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation, including image regions beyond human recognition capability in the evaluation in a principled fashion; 3) the Dark Zurich dataset, comprising 2416 unlabeled nighttime and 2920 unlabeled twilight images with correspondences to their daytime counterparts plus a set of 201 nighttime images with fine pixel-level annotations created with our protocol, which serves as a first benchmark for our novel evaluation. Experiments show that our map-guided curriculum adaptation significantly outperforms state-of-the-art methods on nighttime sets both for standard metrics and our uncertainty-aware metric. Furthermore, our uncertainty-aware evaluation reveals that selective invalidation of predictions can improve results on data with ambiguous content such as our benchmark and profit safety-oriented applications involving invalid inputs.

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Section: Related Workmentioning

confidence: 99%

Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation

Sakaridis

Dai

Gool

2022

IEEE Trans. Pattern Anal. Mach. Intell.

102

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“…Performance of semantic segmentation on daytime scenes has increased rapidly in recent years. As a consequence, attention is now turning to adaptation to adverse conditions [3,11,33,35]. A case in point are recent efforts to adapt clear-weather models to fog [7,26,27], by using both labeled synthetic images and unlabeled real images of increasing fog density.…”

Section: Related Workmentioning

confidence: 99%

Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation

Sakaridis

Dai

Gool

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

208

122

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“…Indeed, conventional imaging fails to characterize objects properly in these complex situations since their visibility is altered. The use of non-conventional sensors is nowadays the best solution to improve road scenes analysis when the conditions are not optimal [2]. Aldibaja et al improve localization accuracy under snow and rain by using a Lidar.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Polarimetric And Color Fusion For Road Scene Analysis In Adverse Weather Conditions

Blin

Aïnouz

Canu

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Road scene analysis is a fundamental task for both autonomous vehicles and ADAS systems. Nowadays, most of autonomous vehicles are able to properly detect objects in good weather conditions; however, some improvements still need to be done when the visibility is altered. Polarimetric imaging is a rich modality that enables to describe an object by its physical information and has recently shown great performances in enhancing road scenes analysis under adverse weather conditions, especially under fog. Besides, it has been shown in a previous work that this modality could be complementary to classical color images especially for car detection. In this work, four different multimodal fusion schemes as well as different color and polarimetric features combinations are explored to achieve a robust scene analysis. The combination of both modalities could be a great asset to describe road scenes when the visibility is altered. Experimental results have shown that, using a well chosen fusion scheme with an adapted features combination, the detection of objects in road scenes under fog was reinforced. The different detection tasks show a significant improvement when using the adapted fusion scheme and features combination. Thanks to the architecture of the fusion scheme and to the properties of the selected features, these results could be extended to other adverse weather conditions.

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Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving

Cited by 69 publications

References 19 publications

Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation

Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation

Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation

Multimodal Polarimetric And Color Fusion For Road Scene Analysis In Adverse Weather Conditions

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