DENSE: Environment Perception in Bad Weather—First Results

Ritter, Walter; Bijelic, Mario; Gruber, Tobias; Kutila, Matti; Holzhüter, Hanno

doi:10.1007/978-3-030-14156-1_12

Cited by 8 publications

(4 citation statements)

References 16 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

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“…The first path would be to utilize already existing sensors that have complementary outputs and enhance the fusion algorithm through deep learning approaches [168]. The second path would be to invest in the sensor hardware technology, as seen in short-wave gated camera and short-wave infrared LiDAR approaches [169]. Both paths have room for further development and enhancement.…”

Section: Harsh Weather Conditionsmentioning

confidence: 99%

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Fayyad

Jaradat

Gruyer

et al. 2020

Sensors

298

130

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Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.

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“…• Sensor performance dependence on environmental/weather conditions [7][8]. Sensor performance degradation needs to be consistent and predictable under changing environmental conditions.…”

Section: Avs and Sensing Challengesmentioning

confidence: 99%