# Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving

arXiv: https://arxiv.org/abs/2205.01532 · HTML: https://mlsanity.com/paper/2205.01532/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 57 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 10 of 10)

- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Ontology based Scene Creation for the Development of Automated Vehicles](https://mlsanity.com/paper/1704.01006/) (2017)
- [Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art](https://mlsanity.com/paper/2108.02403/) (2021)
- [6-Layer Model for a Structured Description and Categorization of Urban Traffic and Environment](https://mlsanity.com/paper/2012.06319/) (2020)
- [On the validation of complex systems operating in open contexts](https://mlsanity.com/paper/1902.10517/) (2019)
- [From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environment](https://mlsanity.com/paper/1905.03989/) (2019)
- [Towards an Ontology for Scenario Definition for the Assessment of Automated Vehicles: An Object-Oriented Framework](https://mlsanity.com/paper/2001.11507/) (2020)
- [A Knowledge-based Approach for the Automatic Construction of Skill Graphs for Online Monitoring](https://mlsanity.com/paper/2102.08827/) (2021)
- [Ph"anomen-Signal-Modell: Formalismus, Graph und Anwendung](https://mlsanity.com/paper/2108.00252/) (2021)
- [Tagging Real-World Scenarios for the Assessment of Autonomous Vehicles](https://mlsanity.com/paper/2012.01081/) (2020)

## Built on by (most cited first, 12 of 12)

- [A Survey on Knowledge Graph-based Methods for Automated Driving](https://mlsanity.com/paper/2210.08119/) (2022)
- [nuScenes Knowledge Graph -- A comprehensive semantic representation of traffic scenes for trajectory prediction](https://mlsanity.com/paper/2312.09676/) (2023)
- [Bridging Data-Driven and Knowledge-Driven Approaches for Safety-Critical Scenario Generation in Automated Vehicle Validation](https://mlsanity.com/paper/2311.10937/) (2023)
- [Grasping Causality for the Explanation of Criticality for Automated Driving](https://mlsanity.com/paper/2210.15375/) (2022)
- [Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation](https://mlsanity.com/paper/2308.12755/) (2023)
- [Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2410.08491/) (2024)
- [Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations](https://mlsanity.com/paper/2403.09668/) (2024)
- [Knowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Survey](https://mlsanity.com/paper/2502.10477/) (2025)
- [Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations](https://mlsanity.com/paper/2403.16908/) (2024)
- [Causality-based Transfer of Driving Scenarios to Unseen Intersections](https://mlsanity.com/paper/2404.02046/) (2024)
- [GraphSCENE: On-Demand Critical Scenario Generation for Autonomous Vehicles in Simulation](https://mlsanity.com/paper/2410.13514/) (2024)
- [Context-Awareness and Interpretability of Rare Occurrences for Discovery and Formalization of Critical Failure Modes](https://mlsanity.com/paper/2504.16117/) (2025)

## Abstract (arXiv)

Knowledge representation and reasoning has a long history of examining how knowledge can be formalized, interpreted, and semantically analyzed by machines. In the area of automated vehicles, recent advances suggest the ability to formalize and leverage relevant knowledge as a key enabler in handling the inherently open and complex context of the traffic world. This paper demonstrates ontologies to be a powerful tool for a) modeling and formalization of and b) reasoning about factors associated with criticality in the environment of automated vehicles. For this, we leverage the well-known 6-Layer Model to create a formal representation of the environmental context. Within this representation, an ontology models domain knowledge as logical axioms, enabling deduction on the presence of critical factors within traffic scenes and scenarios. For executing automated analyses, a joint description logic and rule reasoner is used in combination with an a-priori predicate augmentation. We elaborate on the modular approach, present a publicly available implementation, and evaluate the method by means of a large-scale drone data set of urban traffic scenarios.