Ontology-Based Map Data Quality Assurance

Qiu, Haonan; Ayara, Adel; Glimm, Birte

doi:10.1007/978-3-030-77385-4_5

Cited by 3 publications

(5 citation statements)

References 15 publications

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“…OpenDRIVE maps are verified in [20], but the approach only evaluates the existence of gaps between succeeding lanes and successor/predecessor relationships. Ontologies are also often used to model road networks [21]- [23]. Whereas in [21] and [22], the focus is on reasoning about complete traffic scenes, including other traffic participants, the focus in [23] is on the verification and repairing of maps using ontologies.…”

Section: A Related Workmentioning

confidence: 99%

“…Ontologies are also often used to model road networks [21]- [23]. Whereas in [21] and [22], the focus is on reasoning about complete traffic scenes, including other traffic participants, the focus in [23] is on the verification and repairing of maps using ontologies. The approach in [23] consists of semantic enrichment, violation detection, and violation handling but only covers non-critical violations, e.g., attribute and topological errors.…”

Section: A Related Workmentioning

confidence: 99%

“…Whereas in [21] and [22], the focus is on reasoning about complete traffic scenes, including other traffic participants, the focus in [23] is on the verification and repairing of maps using ontologies. The approach in [23] consists of semantic enrichment, violation detection, and violation handling but only covers non-critical violations, e.g., attribute and topological errors. The authors of [24] use description logic, which is often combined with ontologies, to perform reasoning about road networks.…”

Section: A Related Workmentioning

confidence: 99%

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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%

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

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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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“…Similar approaches based on the (non-semantic) graph representation for scene segmentation can be found in [10,21,100,96,105]. [56,15] Map representation [98] Decision making [87] Context learning [112] Map integration [84] Rules [117,115,116] KG-scene-graphs [114,34] Map updating [85] Reasoning [48,113] KG-based detection [110] Quality of maps [86] Rule learning [19,47,77] Common-sense [17] Scene understanding KG from text [22] Road sign recog. [59,76] Context model [107] Validation Lane detection [72,46] Situation understanding [38] Risk assessm.…”

Section: Semantic Segmentationmentioning

confidence: 99%

“…How to use different types of rules to achieve two-dimensional reasoning is detailed in [85]. Qiu et al [86] addressed the issue of quality assurance in ontology-based map data for AD, specifically the detection and rectification of map errors.…”

Section: 3 Mappingmentioning

confidence: 99%

A Survey on Knowledge Graph-based Methods for Automated Driving

Luettin¹,

Monka²,

Henson³

et al. 2022

Preprint

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Automated driving is one of the most active research areas in computer science. Deep learning methods have made remarkable breakthroughs in machine learning in general and in automated driving (AD) in particular. However, there are still unsolved problems to guarantee reliability and safety of automated systems, especially to effectively incorporate all available information and knowledge in the driving task. Knowledge graphs (KG) have recently gained significant attention from both industry and academia for applications that benefit by exploiting structured, dynamic, and relational data. The complexity of graph-structured data with complex relationships and inter-dependencies between objects has posed significant challenges to existing machine learning algorithms. However, recent progress in knowledge graph embeddings and graph neural networks allows to applying machine learning to graph-structured data. Therefore, we motivate and discuss the potential benefit of KGs applied to the main tasks of AD including 1) ontologies 2) perception, 3) scene understanding, 4) motion planning, and 5) validation. Then, we survey, analyze and categorize ontologies and KG-based approaches for AD. We discuss current research challenges and propose promising future research directions for KG-based solutions for AD.

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