# Scenarios for Development, Test and Validation of Automated Vehicles

arXiv: https://arxiv.org/abs/1801.08598 · HTML: https://mlsanity.com/paper/1801.08598/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2018.

## Built on by (most cited first, 30 of 48)

- [Exploiting symmetry in variational quantum machine learning](https://mlsanity.com/paper/2205.06217/) (2022)
- [Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?](https://mlsanity.com/paper/2404.07569/) (2024)
- [Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis](https://mlsanity.com/paper/2506.11526/) (2025)
- [Description of Corner Cases in Automated Driving: Goals and Challenges](https://mlsanity.com/paper/2109.09607/) (2021)
- [Learning Realistic Traffic Agents in Closed-loop](https://mlsanity.com/paper/2311.01394/) (2023)
- [A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation](https://mlsanity.com/paper/2112.00964/) (2021)
- [Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles](https://mlsanity.com/paper/2109.06126/) (2021)
- [Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test](https://mlsanity.com/paper/2503.02911/) (2025)
- [Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review](https://mlsanity.com/paper/2110.08664/) (2021)
- [RiskBench: A Scenario-based Benchmark for Risk Identification](https://mlsanity.com/paper/2312.01659/) (2023)
- [Learning to Drive via Asymmetric Self-Play](https://mlsanity.com/paper/2409.18218/) (2024)
- [A Review of Testing Object-Based Environment Perception for Safe Automated Driving](https://mlsanity.com/paper/2102.08460/) (2021)
- [Domain Knowledge Distillation from Large Language Model: An Empirical Study in the Autonomous Driving Domain](https://mlsanity.com/paper/2307.11769/) (2023)
- [A Scenario-Based Functional Testing Approach to Improving DNN Performance](https://mlsanity.com/paper/2307.07083/) (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)
- [On the Need for a Statistical Foundation in Scenario-Based Testing of Autonomous Vehicles](https://mlsanity.com/paper/2505.02274/) (2025)
- [Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity](https://mlsanity.com/paper/2105.07639/) (2021)
- [Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness](https://mlsanity.com/paper/2403.14641/) (2024)
- [Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios](https://mlsanity.com/paper/2110.02892/) (2021)
- [ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving](https://mlsanity.com/paper/2209.10078/) (2022)
- [Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning](https://mlsanity.com/paper/2402.19275/) (2024)
- [An Application of Scenario Exploration to Find New Scenarios for the Development and Testing of Automated Driving Systems in Urban Scenarios](https://mlsanity.com/paper/2205.08202/) (2022)
- [Learning Critical Scenarios in Feedback Control Systems for Automated Driving](https://mlsanity.com/paper/2209.12586/) (2022)
- [LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization](https://mlsanity.com/paper/2412.00517/) (2024)
- [Learning Isometric Embeddings of Road Networks using Multidimensional Scaling](https://mlsanity.com/paper/2504.17534/) (2025)
- [A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving](https://mlsanity.com/paper/2506.14100/) (2025)
- [An Evolving Scenario Generation Method based on Dual-modal Driver Model Trained by Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2508.02027/) (2025)
- [DiCriTest: Testing Scenario Generation for Decision-Making Agents Considering Diversity and Criticality](https://mlsanity.com/paper/2508.11514/) (2025)
- [LAMBDA: Covering the Solution Set of Black-Box Inequality by Search Space Quantization](https://mlsanity.com/paper/2203.13708/) (2022)

## Abstract (arXiv)

The ISO 26262 standard from 2016 represents the state of the art for a safety-guided development of safety-critical electric/electronic vehicle systems. These vehicle systems include advanced driver assistance systems and vehicle guidance systems. The development process proposed in the ISO 26262 standard is based upon multiple V-models, and defines activities and work products for each process step. In many of these process steps, scenario based approaches can be applied to achieve the defined work products for the development of automated driving functions. To accomplish the work products of different process steps, scenarios have to focus on various aspects like a human understandable notation or a description via time-space variables. This leads to contradictory requirements regarding the level of detail and way of notation for the representation of scenarios. In this paper, the authors present requirements for the representation of scenarios in different process steps defined by the ISO 26262 standard, propose a consistent terminology based on prior publications for the identified levels of abstraction, and demonstrate how scenarios can be systematically evolved along the phases of the development process outlined in the ISO 26262 standard.