Online reliability assessment and reliability-aware fusion for Ego-Lane detection using influence diagram and Bayes filter

Nguyen, Tran Tuan; Spehr, Jens; Xiong, Jian; Baum, Marcus; Zug, Sebastian; Kruse, Rudolf

doi:10.1109/mfi.2017.8170400

Cited by 10 publications

(9 citation statements)

References 24 publications

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“…So far, other methods, such as Baysian inference, the Dempster-Shafer theory [35], or neural networks have been used for reliability estimation [24], [23], [8]. These methods come with the drawback that they either cannot explicitly differ between statistical evidence and prior or that unintuitive results occur in case of strongly contradicting information sources [40].…”

Section: Related Workmentioning

confidence: 99%

“…In this example, probabilistic inference can never conclude that the coin is fair as a frequentist probability of 0.5 for each side of the coin can never result. Meth-ods like [23,24] based on the evidence theory [35], in turn, have an explicit representation of statistical uncertainty. However, they suffer from unintuitive or even wrong results when the incoming information is highly contradicting [40].…”

Section: Introductionmentioning

confidence: 99%

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Subjective logic reasoning: an urn model intuition and application to connected automated driving

Müller

Buchholz

2021

At - Automatisierungstechnik

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Subjective Logic (SL) is a powerful extend of classical probability theory that can handle small sample sizes and, with that, the resulting statistical uncertainty. However, SL is a quite abstract theory and has found limited attention in the field of automation so far. In this work, we present a new urn model intuition to SL that connects SL with the Pólya urn scheme. The application of SL-based reliability estimation in automation is demonstrated on two examples from the domain of connected automated driving: first to assess external information for motion planning on-board the vehicle and second to rate connected vehicles as agents within a large-scale multi-agent system.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Subjective logic reasoning: an urn model intuition and application to connected automated driving

Müller

Buchholz

2021

At - Automatisierungstechnik

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“…In recent years, some methods based on LiDAR [27], [28] or fusion of LiDAR and camera [29], [30] have also been used for egolane detection. At the same time, since different sensors have different drawbacks, an online sensor reliability assessment and reliability-aware fusion method was proposed for egolane detection [31]. This paper focuses on solving the feature missing problem by using the road shape prior provided by OSM as the lane model.…”

Section: Related Workmentioning

confidence: 99%

Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios

et al. 2020

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As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios. However, ego-lane detection in the missing feature scenarios is still an unsolved problem. To address this problem, previous methods have been devoted to proposing more complicated feature extraction algorithms, but they are very time-consuming and cannot deal with extreme scenarios. Different from others, this paper exploits prior knowledge contained in digital maps, which has a strong capability to enhance the performance of detection algorithms. Specifically, we employ the road shape extracted from OpenStreetMap as lane model, which is highly consistent with the real lane shape and irrelevant to lane features. In this way, only a few lane features are needed to eliminate the position error between the road shape and the real lane, and a search-based optimization algorithm is proposed. Experiments show that the proposed method can be applied to various scenarios and can run in real-time at a frequency of 20 Hz. At the same time, we evaluated the proposed method on the public KITTI Lane dataset where it achieves state-of-the-art performance. Moreover, our code will be open source after publication. INDEX TERMS Ego-lane detection, missing feature, OpenStreetMap, parameter estimation.

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“…In the automotive domain, integrity monitoring for IVs is used to monitor digital map reliability using a datadriven classifier approach [5] and to estimate and assure lane information quality [6], including probabilistic fusion from multiple sources using the Dempster-Shafer (DS) theory of evidence [7], [8]. While being effective in practice, for their specific use-case, these approaches are tailored towards use of data that is generated from on-board sensors within an IV.…”

Section: Arxiv:190301556v2 [Eesssp] 22 May 2019mentioning

confidence: 99%

“…We, on the other hand, propose a solution that uses information from the outside and additionally is simple in terms of computational complexity. Lastly, our approach relies on the theory of SL [2], thus does not suffer from the drawback of inconsistent system behavior in some cases due to weaknesses in the underlying DS-based fusion [7], [8]. In comparison to [9], our system takes into account multiple information sources and yields continuous reliabilities as well as their estimation uncertainties rather than a binary classification as part of the fusion process.…”

Section: Arxiv:190301556v2 [Eesssp] 22 May 2019mentioning

confidence: 99%

A Subjective-Logic-based Reliability Estimation Mechanism for Cooperative Information with Application to IV's Safety

Müller

Gabb

Buchholz

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Use of cooperative information, distributed by road-side units, offers large potential for intelligent vehicles (IVs). As vehicle automation progresses and cooperative perception is used to fill the blind spots of onboard sensors, the question of reliability of the data becomes increasingly important in safety considerations (SOTIF, Safety of the Intended Functionality).This paper addresses the problem to estimate the reliability of cooperative information for in-vehicle use. We propose a novel method to infer the reliability of received data based on the theory of Subjective Logic (SL). Using SL, we fuse multiple information sources, which individually only provide mild cues of the reliability, into a holistic estimate, which is statistically sound through an end-to-end modeling within the theory of SL.Using the proposed scheme for probabilistic SL-based fusion, IVs are able to separate faulty from correct data samples with a large margin of safety. Real world experiments show the applicability and effectiveness of our approach.

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Online reliability assessment and reliability-aware fusion for Ego-Lane detection using influence diagram and Bayes filter

Cited by 10 publications

References 24 publications

Subjective logic reasoning: an urn model intuition and application to connected automated driving

Subjective logic reasoning: an urn model intuition and application to connected automated driving

Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios

A Subjective-Logic-based Reliability Estimation Mechanism for Cooperative Information with Application to IV's Safety

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