An Experimental Analysis of Rain Interference on Detection and Ranging Sensors

Vriesman, Daniel; Thoresz, Bernhard; Steinhauser, Dagmar; Zimmer, Alessandro; Britto, Alceu S.; Brandmeier, Thomas

doi:10.1109/itsc45102.2020.9294505

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…a Lidar, can indicate the presence of degrading environments e.g. rain or fog [42,43,44] and hence sensor impairment. We refer to this sort of interoception as sensor "introspection".…”

Section: Discussionmentioning

confidence: 99%

Safety Assessment for Autonomous Systems' Perception Capabilities

Molloy¹,

McDermid²

2022

Preprint

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Autonomous Systems (AS) are increasingly proposed, or used, in Safety Critical (SC) applications. Many such systems make use of sophisticated sensor suites and processing to provide scene understanding which informs the AS' decision-making. The sensor processing typically makes use of Machine Learning (ML) and has to work in challenging environments, further the ML-algorithms have known limitations,e.g., the possibility of false-negatives or false-positives in object classification. The well-established safety-analysis methods developed for conventional SC systems are not well-matched to AS, ML, or the sensing systems used by AS. This paper proposes an adaptation of well-established safety-analysis methods to address the specifics of perception-systems for AS, including addressing environmental effects and the potential failure-modes of ML, and provides a rationale for choosing particular sets of guidewords, or prompts, for safety-analysis. It goes on to show how the results of the analysis can be used to inform the design and verification of the AS and illustrates the new method by presenting a partial analysis of a road vehicle. Illustrations in the paper are primarily based on optical sensing, however the paper discusses the applicability of the method to other sensing modalities and its role in a wider safety process addressing the overall capabilities of AS.

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“…a Lidar, can indicate the presence of degrading environments e.g. rain or fog [42,43,44] and hence sensor impairment. We refer to this sort of interoception as sensor "introspection".…”

Section: Discussionmentioning

confidence: 99%

Safety Assessment for Autonomous Systems' Perception Capabilities

Molloy¹,

McDermid²

2022

Preprint

View full text Add to dashboard Cite

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“…It distinguishes targets from clutter by setting a certain power threshold, above which is determined as a target. Various sources of interference, including internal device noise, ground features, water waves, rain, and snow, are constantly presented when detecting target signals [107,108]. In an automatic detection system, when the detection threshold was set, the likelihood of false alarms increased as interference intensity rose.…”

Section: Cfarmentioning

confidence: 99%

Radar Technology for River Flow Monitoring: Assessment of the Current Status and Future Challenges

Huang

Chen

Liu

et al. 2023

Water

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As an emerging non-contact method of flow monitoring, radar technology compensates for the shortcomings of traditional methods in terms of the efficiency, timeliness, and difficulty in monitoring high floods, and can provide accurate measurement results, making it one of the most promising flow monitoring methods in the future. This paper describes in detail the workflow from radar data acquisition to flow calculation; compares and analyzes the current state-of-the-art signal sampling and its limitations, Doppler spectrum estimation, signal processing and flow inversion; points out the challenges that these technologies may face in the future; and provides corresponding solutions in order to improve the real-time and accuracy of hydrometric as well as promote the development of non-contact flow monitoring technology.

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