[Paper] Can AI Generate more Comprehensive Test Scenarios? Review on Automated Driving Systems Test Scenario Generation Methods
Published: (December 17, 2025 at 08:14 AM EST)
4 min read
Source: arXiv
Source: arXiv - 2512.15422v1
Overview
The paper surveys the state‑of‑the‑art in generating test scenarios for Automated Driving Systems (ADS). By comparing traditional expert‑driven methods with the latest AI‑powered generative techniques, the authors expose both the promise of AI‑assisted testing and the gaps that still need to be filled before these approaches can be trusted in production‑level safety pipelines.
Key Contributions
- Comprehensive review (2015‑2025) of 31 primary studies and 10 prior surveys, with a deep dive into the most recent (2023‑2025) AI‑driven frameworks.
- Refined taxonomy that extends existing classifications to cover multimodal data (e.g., LiDAR, radar, camera, V2X) and operational design domains (ODDs).
- Ethical & safety checklist for responsible scenario generation, addressing bias, privacy, and human‑factor considerations.
- ODD coverage map & difficulty schema that visualizes how well current methods span different driving contexts (urban, highway, adverse weather, etc.) and scenario complexities.
- Identification of three persistent research gaps: lack of standardized evaluation metrics, limited ethical/human‑factor integration, and insufficient multimodal/ODD‑specific coverage.
Methodology
- Systematic literature search – The authors queried major databases (IEEE Xplore, ACM DL, Scopus) using keywords around “ADS testing”, “scenario generation”, and “AI”. Papers from 2015‑2025 were screened for relevance, yielding 31 primary studies.
- Categorization – Each study was classified by its underlying technique (expert knowledge, ontology, naturalistic data, GAN, diffusion model, LLM, RL, etc.) and by the modalities it supports (single‑sensor vs. multimodal).
- Comparative synthesis – The authors built a matrix that cross‑references methods with evaluation criteria (realism, diversity, safety‑criticality, computational cost).
- Gap analysis – By overlaying the matrix with the newly proposed taxonomy, they highlighted where current research falls short, especially regarding standard metrics and ethical safeguards.
- Deliverables – The taxonomy, checklist, and ODD map were distilled into actionable artifacts that can be directly adopted by researchers or industry teams.
Results & Findings
| Aspect | Traditional Approaches | Recent AI‑Driven Approaches |
|---|---|---|
| Source of scenarios | Expert rules, ontologies, naturalistic driving data, accident reports | Large Language Models (LLMs), GANs, Diffusion Models, Reinforcement Learning (RL) |
| Diversity & coverage | Limited to predefined rule sets; struggles with rare edge cases | Capable of synthesizing rare, safety‑critical edge cases on demand |
| Multimodal support | Mostly single‑sensor (camera) or hand‑crafted sensor fusion | Native generation of synchronized LiDAR, radar, camera, V2X streams |
| Scalability | Manual tuning, high human effort | Automated, data‑driven, can generate thousands of scenarios in minutes |
| Evaluation standards | Ad‑hoc metrics (e.g., scenario count, visual inspection) | No consensus yet; authors call for unified benchmarks |
The authors conclude that AI‑based generators dramatically improve scenario diversity and scalability, but the community still lacks standardized, reproducible evaluation metrics and ethical guardrails.
Practical Implications
- Accelerated testing pipelines – Developers can plug generative models into simulation environments (e.g., CARLA, LGSVL) to auto‑populate test suites with high‑risk corner cases, reducing reliance on costly on‑road miles.
- Continuous safety regression – With RL or diffusion models, new scenarios can be generated on‑the‑fly as the ADS software evolves, enabling “continuous integration” style safety testing.
- Better ODD validation – The ODD coverage map helps product managers verify that a system’s intended operational domain (e.g., night‑time urban driving) is adequately exercised before release.
- Ethical compliance – The checklist provides a concrete set of questions (bias in training data, privacy of recorded accidents, human‑factor realism) that can be integrated into internal QA processes or regulatory submissions.
- Benchmarking & competition – The taxonomy and difficulty schema lay the groundwork for open challenges (e.g., “Scenario Generation Track” at CVPR/ICRA) where teams can compare methods on a common footing.
Limitations & Future Work
- Metric vacuum – While the paper stresses the need for standardized metrics, it does not propose concrete ones, leaving the community to define them.
- Data dependency – AI generators still rely on large, high‑quality datasets; gaps in diverse sensor recordings can bias the generated scenarios.
- Human‑factor modeling – Ethical and safety checklists are high‑level; detailed models of driver behavior, pedestrian intent, or cultural driving norms remain underexplored.
- Real‑world validation – Most evaluated methods are tested only in simulation; bridging the sim‑to‑real gap (e.g., via domain adaptation) is an open research avenue.
Future work should focus on establishing benchmark suites with agreed‑upon metrics, expanding multimodal datasets (especially for adverse weather and rare ODDs), and integrating human‑in‑the‑loop validation to ensure that AI‑generated scenarios are not just diverse, but also faithfully represent real‑world safety challenges.
Authors
- Ji Zhou
- Yongqi Zhao
- Yixian Hu
- Hexuan Li
- Zhengguo Gu
- Nan Xu
- Arno Eichberger
Paper Information
- arXiv ID: 2512.15422v1
- Categories: cs.SE
- Published: December 17, 2025
- PDF: Download PDF