Cover Image

An LLM-driven Scenario Generation Pipeline Using an Extended Scenic DSL for Autonomous Driving Safety Validation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Safa, Fida Khandaker, Jiang, Yupeng, Zheng, Xi
Format:	Preprint
Published:	2026
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Regression Testing Optimization for ROS-based Autonomous Systems: A Comprehensive Review of Techniques
by: Jiang, Yupeng, et al.
Published: (2025)

TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles via Validated LLM-Guided Knowledge Extraction
by: Deng, Yao, et al.
Published: (2023)

From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing
by: Liang, Linfeng, et al.
Published: (2026)

A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
by: Jiang, Yupeng, et al.
Published: (2025)

LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos
by: Tian, Haoxiang, et al.
Published: (2024)

ScenicNL: Generating Probabilistic Scenario Programs from Natural Language
by: Elmaaroufi, Karim, et al.
Published: (2024)

MARL-OT: Multi-Agent Reinforcement Learning Guided Online Fuzzing to Detect Safety Violation in Autonomous Driving Systems
by: Liang, Linfeng, et al.
Published: (2025)

Risk Scenario Generation for Autonomous Driving Systems based on Causal Bayesian Networks
by: Zhao, Jiangnan, et al.
Published: (2024)

Modeling Language for Scenario Development of Autonomous Driving Systems
by: Aoki, Toshiaki, et al.
Published: (2025)

From Text to DSL: Evaluating Grammar-Based Model Generation Using Open LLMs
by: Baber, Junaid, et al.
Published: (2026)

AutoMT: A Multi-Agent LLM Framework for Automated Metamorphic Testing of Autonomous Driving Systems
by: Liang, Linfeng, et al.
Published: (2025)

Txt2Sce: Scenario Generation for Autonomous Driving System Testing Based on Textual Reports
by: Ji, Pin, et al.
Published: (2025)

DSL or Code? Evaluating the Quality of LLM-Generated Algebraic Specifications: A Case Study in Optimization at Kinaxis
by: Ayoughi, Negin, et al.
Published: (2026)

Towards Automated Driving Violation Cause Analysis in Scenario-Based Testing for Autonomous Driving Systems
by: Wan, Ziwen, et al.
Published: (2024)

Causality-aware Safety Testing for Autonomous Driving Systems
by: Tang, Wenbing, et al.
Published: (2025)

Fine-grained Testing for Autonomous Driving Software: a Study on Autoware with LLM-driven Unit Testing
by: Wang, Wenhan, et al.
Published: (2025)

Perception-Guided Fuzzing for Simulated Scenario-Based Testing of Autonomous Driving Systems
by: Pham, Tri Minh Triet, et al.
Published: (2024)

Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test
by: Cai, Xuan, et al.
Published: (2025)

SoVAR: Building Generalizable Scenarios from Accident Reports for Autonomous Driving Testing
by: Guo, An, et al.
Published: (2024)

LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language Models
by: Tang, Shuncheng, et al.
Published: (2024)

Towards a DSL to Formalize Multimodal Requirements
by: Gomez-Vazquez, Marcos, et al.
Published: (2025)

Multi-modal Traffic Scenario Generation for Autonomous Driving System Testing
by: Tu, Zhi, et al.
Published: (2025)

SAFE: Harnessing LLM for Scenario-Driven ADS Testing from Multimodal Crash Data
by: Luo, Siwei, et al.
Published: (2025)

An Event-Driven Tool for Context-Aware Code Smell Detection Using SmellDSL
by: Viegas, Matheus dos Santos, et al.
Published: (2026)

AugmenTest: Enhancing Tests with LLM-Driven Oracles
by: Khandaker, Shaker Mahmud, et al.
Published: (2025)

Dynasto: Validity-Aware Dynamic-Static Parameter Optimization for Autonomous Driving Testing
by: Humeniuk, Dmytro, et al.
Published: (2026)

Can AI Generate more Comprehensive Test Scenarios? Review on Automated Driving Systems Test Scenario Generation Methods
by: Zhou, Ji, et al.
Published: (2025)

On-Demand Scenario Generation for Testing Automated Driving Systems
by: Yan, Songyang, et al.
Published: (2025)

GOOSE: Goal-Conditioned Reinforcement Learning for Safety-Critical Scenario Generation
by: Ransiek, Joshua, et al.
Published: (2024)

Automated Repair of TEE Partitioning Issues via DSL-Guided and LLM-Assisted Patching
by: Ma, Chengyan, et al.
Published: (2026)

$μ$Drive: User-Controlled Autonomous Driving
by: Wang, Kun, et al.
Published: (2024)

UniSTPA: A Safety Analysis Framework for End-to-End Autonomous Driving
by: Kou, Hongrui, et al.
Published: (2025)

On Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Software
by: Nouri, Ali, et al.
Published: (2025)

From Conflicts to Collisions: A Two-Stage Collision Scenario-Testing Approach for Autonomous Driving Systems
by: Chen, Siyuan, et al.
Published: (2026)

RoadGen: Generating Road Scenarios for Autonomous Vehicle Testing
by: Yang, Fan, et al.
Published: (2024)

Towards Automated Governance: A DSL for Human-Agent Collaboration in Software Projects
by: Ait, Adem, et al.
Published: (2025)

Still Manual? Automated Linter Configuration via DSL-Based LLM Compilation of Coding Standards
by: Zhang, Zejun, et al.
Published: (2026)

Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study
by: Chand, Sivajeet, et al.
Published: (2026)

Key Safety Design Overview in AI-driven Autonomous Vehicles
by: Vyas, Vikas, et al.
Published: (2024)

Declarative Policy Control for Data Spaces: A DSL-Based Approach for Manufacturing-X
by: Pfeiffer, Jérôme, et al.
Published: (2025)