# Ontology based Scene Creation for the Development of Automated Vehicles

arXiv: https://arxiv.org/abs/1704.01006 · HTML: https://mlsanity.com/paper/1704.01006/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2017.

## Built on by (most cited first, 30 of 40)

- [ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles](https://mlsanity.com/paper/2405.14062/) (2024)
- [SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles](https://mlsanity.com/paper/2206.09682/) (2022)
- [Synthetic Datasets for Autonomous Driving: A Survey](https://mlsanity.com/paper/2304.12205/) (2023)
- [Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?](https://mlsanity.com/paper/2404.07569/) (2024)
- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation](https://mlsanity.com/paper/2112.00964/) (2021)
- [Towards Traffic Scene Description: The Semantic Scene Graph](https://mlsanity.com/paper/2111.10196/) (2021)
- [Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving](https://mlsanity.com/paper/2205.01532/) (2022)
- [A Review of Testing Object-Based Environment Perception for Safe Automated Driving](https://mlsanity.com/paper/2102.08460/) (2021)
- [A Survey on Knowledge Graph-based Methods for Automated Driving](https://mlsanity.com/paper/2210.08119/) (2022)
- [Domain Knowledge Distillation from Large Language Model: An Empirical Study in the Autonomous Driving Domain](https://mlsanity.com/paper/2307.11769/) (2023)
- [(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learning](https://mlsanity.com/paper/2302.13726/) (2023)
- [Make Full Use of Testing Information: An Integrated Accelerated Testing and Evaluation Method for Autonomous Driving Systems](https://mlsanity.com/paper/2501.11924/) (2025)
- [How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?](https://mlsanity.com/paper/2301.04414/) (2023)
- [SAFR-AV: Safety Analysis of Autonomous Vehicles using Real World Data -- An end-to-end solution for real world data driven scenario-based testing for pre-certification of AV stacks](https://mlsanity.com/paper/2302.14601/) (2023)
- [Bridging Data-Driven and Knowledge-Driven Approaches for Safety-Critical Scenario Generation in Automated Vehicle Validation](https://mlsanity.com/paper/2311.10937/) (2023)
- [When Digital Twins Meet Large Language Models: Realistic, Interactive, and Editable Simulation for Autonomous Driving](https://mlsanity.com/paper/2507.00319/) (2025)
- [A Knowledge-based Approach for the Automatic Construction of Skill Graphs for Online Monitoring](https://mlsanity.com/paper/2102.08827/) (2021)
- [Self Supervised Clustering of Traffic Scenes using Graph Representations](https://mlsanity.com/paper/2211.15508/) (2022)
- [Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity](https://mlsanity.com/paper/2402.01795/) (2024)
- [Dance of the ADS: Orchestrating Failures through Historically-Informed Scenario Fuzzing](https://mlsanity.com/paper/2407.04359/) (2024)
- [Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2410.08491/) (2024)
- [LLM-based Realistic Safety-Critical Driving Video Generation](https://mlsanity.com/paper/2507.01264/) (2025)
- [Graph Modeling in Computer Assisted Automotive Development](https://mlsanity.com/paper/2209.14910/) (2022)
- [Traffic Scenario Logic: A Spatial-Temporal Logic for Modeling and Reasoning of Urban Traffic Scenarios](https://mlsanity.com/paper/2405.13715/) (2024)
- [Flow to Rare Events: An Application of Normalizing Flow in Temporal Importance Sampling for Automated Vehicle Validation](https://mlsanity.com/paper/2407.07320/) (2024)
- [Learning Isometric Embeddings of Road Networks using Multidimensional Scaling](https://mlsanity.com/paper/2504.17534/) (2025)
- [Planning Automated Driving with Accident Experience Referencing and Common-sense Inferencing](https://mlsanity.com/paper/2301.10892/) (2023)
- [Towards Establishing Systematic Classification Requirements for Automated Driving](https://mlsanity.com/paper/2307.14058/) (2023)
- [Causality-based Transfer of Driving Scenarios to Unseen Intersections](https://mlsanity.com/paper/2404.02046/) (2024)

## Abstract (arXiv)

The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.