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Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test

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arxiv 2503.02911 v1 pith:SEHVJKL7 submitted 2025-03-04 cs.SE cs.AIcs.CL

Xuan Cai , Xuesong Bai , Zhiyong Cui , Danmu Xie , Daocheng Fu , Haiyang Yu , Yilong Ren This is my paper · ORCID
classification cs.SEcs.AIcs.CL
keywords scenarioscenariostestlanguagetext2scenariouserautonomousclosely
verification ladder T0 review T1 audit T2 compute T3 formal
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Autonomous driving (AD) testing constitutes a critical methodology for assessing performance benchmarks prior to product deployment. The creation of segmented scenarios within a simulated environment is acknowledged as a robust and effective strategy; however, the process of tailoring these scenarios often necessitates laborious and time-consuming manual efforts, thereby hindering the development and implementation of AD technologies. In response to this challenge, we introduce Text2Scenario, a framework that leverages a Large Language Model (LLM) to autonomously generate simulation test scenarios that closely align with user specifications, derived from their natural language inputs. Specifically, an LLM, equipped with a meticulously engineered input prompt scheme functions as a text parser for test scenario descriptions, extracting from a hierarchically organized scenario repository the components that most accurately reflect the user's preferences. Subsequently, by exploiting the precedence of scenario components, the process involves sequentially matching and linking scenario representations within a Domain Specific Language corpus, ultimately fabricating executable test scenarios. The experimental results demonstrate that such prompt engineering can meticulously extract the nuanced details of scenario elements embedded within various descriptive formats, with the majority of generated scenarios aligning closely with the user's initial expectations, allowing for the efficient and precise evaluation of diverse AD stacks void of the labor-intensive need for manual scenario configuration. Project page: https://caixxuan.github.io/Text2Scenario.GitHub.io.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles

    cs.CV 2025-08 conditional novelty 6.0 of 10

    ScenGE generates more collision-prone autonomous driving test scenarios by combining LLM-suggested adversarial events with optimized background traffic, beating prior generators on CARLA benchmarks.

  2. AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models

    cs.AI 2025-08 conditional novelty 6.0 of 10

    Large language models prompted with a graph encoding of airspace reliably generate non-interacting aircraft scenarios and can control the type and location of interactions, with the best models near zero errors.

  3. From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving

    cs.CV 2025-05 reject novelty 6.0 of 10

    SERA uses LLM-driven failure analysis and scenario retrieval to select training scenarios for few-shot fine-tuning, improving simulated autonomous driving scores.

  4. CrashAgent: Crash Scenario Generation via Multi-modal Reasoning

    cs.RO 2025-05 conditional novelty 6.0 of 10

    A multi-agent vision-language framework converts NHTSA crash reports into simulation-ready road layouts and collision scenarios, with modest accuracy gains over direct VLM baselines.

  5. Gaussian Field Representations for Turbulent Flow: Compression, Scale Separation, and Physical Fidelity

    physics.flu-dyn 2026-04 unverdicted novelty 5.0 of 10

    Gaussian primitives compress 3D Taylor-Green vortex flows at ratios over 1000x while preserving velocity but degrading enstrophy, with anisotropic extensions recovering small-scale vortical structures better than base...

  6. A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data

    cs.AI 2026-01 conditional novelty 5.0 of 10

    A metric-oriented survey that classifies intrinsic quality and trustworthiness metrics for LLM-generated data across six modalities and documents systematic evaluation gaps in the current literature.

  7. Generative AI for Testing of Autonomous Driving Systems: A Survey

    cs.SE 2025-08 conditional novelty 5.0 of 10

    A systematic survey that organizes 91 studies of generative AI for autonomous driving testing into six scenario-based tasks and catalogs 27 limitations.

  8. A Survey on the Applications of Generative Artificial Intelligence in Automated Driving Systems Test Scenario Generation Methods

    cs.SE 2025-12 reject novelty 4.0 of 10

    A literature survey of scenario-generation methods for ADS testing that adds an unvalidated AII/RAS/OCS metric suite and ODD-difficulty schema, undermined by inconsistent calculations in the worked examples.

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