Terminology and Analysis of Map Deviations in Urban Domains: Towards Dependability for HD Maps in Automated Vehicles

Plachetka, Christopher; Maier, Niels; Fricke, Jenny; Termöhlen, Jan-Aike; Fingscheidr, Tim

doi:10.1109/iv47402.2020.9304580

Cited by 10 publications

(2 citation statements)

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“…In [17], the change of a map over several years is evaluated, which shows the necessity of map validation. The authors of [18] introduce terminology and metrics for map deviations. In map verification, the map is verified for consistency, i.e., whether the map fulfills a specification.…”

Section: A Related Workmentioning

confidence: 99%

Map Verification and Repairing Using Formalized Map Specifications

Maierhofer,

Ballnath,

Althoff

2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Autonomous vehicles benefit from correct maps to participate in traffic safely, but often maps are not verified before their usage. We address this problem by providing an approach to verify and repair maps automatically based on a formalization of map specifications in higher-order logic. Unlike previous work, we provide a collection of map specifications. We can verify and repair all possible map parts, from geometric to semantic elements, e.g., adjacency relationships, lane types, road boundaries, traffic signs, and intersections. Due to the modular design of our approach, one can integrate additional logics. We compare ontologies, answer set programming, and satisfiability modulo theories with our higher-order logic verification algorithm. Our evaluation shows that our approach can efficiently verify and repair maps from several data sources and of different map sizes. We provide our tool as part of the CommonRoad Scenario Designer toolbox available at commonroad.in.tum.de.

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Section: A Related Workmentioning

confidence: 99%

Map Verification and Repairing Using Formalized Map Specifications

Maierhofer,

Ballnath,

Althoff

2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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“…Consequently, this survey focuses on UDA methods for environment perception in autonomous driving, particularly on semantic segmentation of camera images. Semantic segmentation not only provides important information about objects but also about the environment surrounding the vehicle (the background classes), which can serve as the basis of a local grid map [7] or for map verification [8], [9], [10]. This makes segmentation a crucial part of the perception system.…”

Section: Introductionmentioning

confidence: 99%

Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving

Schwonberg¹,

Niemeijer

Termöhlen

et al. 2023

IEEE Access

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Deep neural networks (DNNs) have proven their capabilities in the past years and play a significant role in environment perception for the challenging application of automated driving. They are employed for tasks such as detection, semantic segmentation, and sensor fusion. Despite tremendous research efforts, several issues still need to be addressed that limit the applicability of DNNs in automated driving. The bad generalization of DNNs to unseen domains is a major problem on the way to a safe, large-scale application, because manual annotation of new domains is costly, particularly for semantic segmentation. For this reason, methods are required to adapt DNNs to new domains without labeling effort. This task is termed unsupervised domain adaptation (UDA). While several different domain shifts challenge DNNs, the shift between synthetic and real data is of particular importance for automated driving, as it allows the use of simulation environments for DNN training. We present an overview of the current state of the art in this research field. We categorize and explain the different approaches for UDA. The number of considered publications is larger than any other survey on this topic. We also go far beyond the description of the UDA state-of-the-art, as we present a quantitative comparison of approaches and point out the latest trends in this field. We conduct a critical analysis of the state-of-the-art and highlight promising future research directions. With this survey, we aim to facilitate UDA research further and encourage scientists to exploit novel research directions.

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