# One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving

arXiv: https://arxiv.org/abs/2209.00342 · HTML: https://mlsanity.com/paper/2209.00342/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2022.

## Built on by (most cited first, 16 of 16)

- [Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models](https://mlsanity.com/paper/2506.09042/) (2025)
- [Adaptive Testing of Computer Vision Models](https://mlsanity.com/paper/2212.02774/) (2022)
- [Towards Knowledge-driven Autonomous Driving](https://mlsanity.com/paper/2312.04316/) (2023)
- [Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey](https://mlsanity.com/paper/2302.02790/) (2023)
- [Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors](https://mlsanity.com/paper/2408.03200/) (2024)
- [AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving](https://mlsanity.com/paper/2405.07865/) (2024)
- [Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving](https://mlsanity.com/paper/2308.05701/) (2023)
- [CC-SGG: Corner Case Scenario Generation using Learned Scene Graphs](https://mlsanity.com/paper/2309.09844/) (2023)
- [Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions](https://mlsanity.com/paper/2402.04168/) (2024)
- [Bridging Data-Driven and Knowledge-Driven Approaches for Safety-Critical Scenario Generation in Automated Vehicle Validation](https://mlsanity.com/paper/2311.10937/) (2023)
- [Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2410.08491/) (2024)
- [Outline of an Independent Systematic Blackbox Test for ML-based Systems](https://mlsanity.com/paper/2401.17062/) (2024)
- [ST-Prune: Training-Free Spatio-Temporal Token Pruning for Vision-Language Models in Autonomous Driving](https://mlsanity.com/paper/2604.19145/) (2026)
- [CLAP: Contrastive Latent-space Prompt Optimization for End-to-end Autonomous Driving](https://mlsanity.com/paper/2605.17284/) (2026)
- [GraphSCENE: On-Demand Critical Scenario Generation for Autonomous Vehicles in Simulation](https://mlsanity.com/paper/2410.13514/) (2024)
- [Probabilistic Modelling of Operational Design Domains, A New Approach for Testing AI Systems](https://mlsanity.com/paper/2609.24397/) (2026)

## Abstract (arXiv)

The core obstacle towards a large-scale deployment of autonomous vehicles currently lies in the long tail of rare events. These are extremely challenging since they do not occur often in the utilized training data for deep neural networks. To tackle this problem, we propose the generation of additional synthetic training data, covering a wide variety of corner case scenarios. As ontologies can represent human expert knowledge while enabling computational processing, we use them to describe scenarios. Our proposed master ontology is capable to model scenarios from all common corner case categories found in the literature. From this one master ontology, arbitrary scenario-describing ontologies can be derived. In an automated fashion, these can be converted into the OpenSCENARIO format and subsequently executed in simulation. This way, also challenging test and evaluation scenarios can be generated.