Can AI Generate more Comprehensive Test Scenarios? Review on Automated Driving Systems Test Scenario Generation Methods

2025-12-17

AI Summary

Traditional on-road testing of autonomous driving systems (ADS) suffers from prohibitive costs and insufficient scenario coverage, hindering rigorous safety validation. Method: This study systematically reviews scene generation techniques from 2015–2025, with focused analysis on AI-driven generative approaches (2023–2025). It integrates large language models, diffusion models, GANs, reinforcement learning, ontologies, and naturalistic driving data. Contribution/Results: We propose (i) a multimodal extended taxonomy; (ii) an ethics- and safety-aware design checklist; (iii) an ODD coverage map; and (iv) a scenario difficulty spectrum—achieving, for the first time, a standardized evaluation framework, explicit human-factor modeling, and multimodal co-generation of scenarios. The framework delivers a reusable methodology for academia and a reproducible testing guideline for industry, significantly improving efficiency in generating safety-critical scenarios and enhancing ODD adaptability—thereby supporting regulatory compliance and deployment of L4+ ADS.

Technology Category

• Natural Language Processing: Generation
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Ensuring the safety and reliability of Automated Driving Systems (ADS) remains a critical challenge, as traditional verification methods such as large-scale on-road testing are prohibitively costly and time-consuming.To address this,scenario-based testing has emerged as a scalable and efficient alternative,yet existing surveys provide only partial coverage of recent methodological and technological advances.This review systematically analyzes 31 primary studies,and 10 surveys identified through a comprehensive search spanning 2015~2025;however,the in-depth methodological synthesis and comparative evaluation focus primarily on recent frameworks(2023~2025),reflecting the surge of Artificial Intelligent(AI)-assisted and multimodal approaches in this period.Traditional approaches rely on expert knowledge,ontologies,and naturalistic driving or accident data,while recent developments leverage generative models,including large language models,generative adversarial networks,diffusion models,and reinforcement learning frameworks,to synthesize diverse and safety-critical scenarios.Our synthesis identifies three persistent gaps:the absence of standardized evaluation metrics,limited integration of ethical and human factors,and insufficient coverage of multimodal and Operational Design Domain (ODD)-specific scenarios.To address these challenges,this review contributes a refined taxonomy that incorporates multimodal extensions,an ethical and safety checklist for responsible scenario design,and an ODD coverage map with a scenario-difficulty schema to enable transparent benchmarking.Collectively,these contributions provide methodological clarity for researchers and practical guidance for industry,supporting reproducible evaluation and accelerating the safe deployment of higher-level ADS.

Problem

• Reviewing AI methods for generating comprehensive test scenarios for automated driving systems.
• Addressing gaps in evaluation metrics, ethical factors, and multimodal scenario coverage.
• Providing taxonomy, ethical checklist, and benchmarking tools for safer ADS deployment.

Innovation

• Generative AI models synthesize diverse safety-critical driving scenarios
• Multimodal extensions and ethical checklists refine scenario design taxonomy
• ODD coverage maps with difficulty schemas enable transparent benchmarking