# Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test arXiv: https://arxiv.org/abs/2503.02911 ยท HTML: https://mlsanity.com/paper/2503.02911/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Placed in Large language models for driving, one of 9 directions in End-to-end autonomous driving. - Builds on 27 of its 60 references that are in the map. - First posted on arXiv 4 Mar 2025. ## Placed in - End-to-end autonomous driving / [Large language models for driving](https://mlsanity.com/driving/large-language-models-for-driving/) ## Builds on (most cited first, 27 of 27) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023) - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022) - [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017) - [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023) - [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022) - [Yi: Open Foundation Models by 01.AI](https://mlsanity.com/paper/2403.04652/) (2024) - [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022) - [Do Large Language Models Know What They Don't Know?](https://mlsanity.com/paper/2305.18153/) (2023) - [Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer](https://mlsanity.com/paper/2207.14024/) (2022) - [Uncertainty Quantification and Deep Ensembles](https://mlsanity.com/paper/2007.08792/) (2020) - [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://mlsanity.com/paper/2305.18486/) (2023) - [Language-Guided Traffic Simulation via Scene-Level Diffusion](https://mlsanity.com/paper/2306.06344/) (2023) - [Scenic: A Language for Scenario Specification and Scene Generation](https://mlsanity.com/paper/1809.09310/) (2018) - [ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling](https://mlsanity.com/paper/2306.12241/) (2023) - [Scenarios for Development, Test and Validation of Automated Vehicles](https://mlsanity.com/paper/1801.08598/) (2018) - [Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios](https://mlsanity.com/paper/2004.06531/) (2020) - [LLM-BRAIn: AI-driven Fast Generation of Robot Behaviour Tree based on Large Language Model](https://mlsanity.com/paper/2305.19352/) (2023) - [Robot Behavior-Tree-Based Task Generation with Large Language Models](https://mlsanity.com/paper/2302.12927/) (2023) - [Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles](https://mlsanity.com/paper/2103.07403/) (2021) - [Testing of Autonomous Driving Systems: Where Are We and Where Should We Go?](https://mlsanity.com/paper/2106.12233/) (2021) - [LawBreaker: An Approach for Specifying Traffic Laws and Fuzzing Autonomous Vehicles](https://mlsanity.com/paper/2208.14656/) (2022) - [A Review of Testing Object-Based Environment Perception for Safe Automated Driving](https://mlsanity.com/paper/2102.08460/) (2021) - [Dialogue-based generation of self-driving simulation scenarios using Large Language Models](https://mlsanity.com/paper/2310.17372/) (2023) - [A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation](https://mlsanity.com/paper/2110.14870/) (2021) - [SAFR-AV: Safety Analysis of Autonomous Vehicles using Real World Data -- An end-to-end solution for real world data driven scenario-based testing for pre-certification of AV stacks](https://mlsanity.com/paper/2302.14601/) (2023) - [A Domain-Specific Language for Simulation-Based Testing of IoT Edge-to-Cloud Solutions](https://mlsanity.com/paper/2208.06954/) (2022) ## Built on by (most cited first, 18 of 18) - [Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis](https://mlsanity.com/paper/2506.11526/) (2025) - [Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Review](https://mlsanity.com/paper/2506.06301/) (2025) - [Bench2ADVLM: A Closed-Loop Benchmark for Vision-language Models in Autonomous Driving](https://mlsanity.com/paper/2508.02028/) (2025) - [A Survey of Inductive Reasoning for Large Language Models](https://mlsanity.com/paper/2510.10182/) (2025) - [A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data](https://mlsanity.com/paper/2601.17717/) (2026) - [CrashAgent: Crash Scenario Generation via Multi-modal Reasoning](https://mlsanity.com/paper/2505.18341/) (2025) - [Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles](https://mlsanity.com/paper/2508.14527/) (2025) - [AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models](https://mlsanity.com/paper/2508.02269/) (2025) - [Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving](https://mlsanity.com/paper/2605.22809/) (2026) - [From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving](https://mlsanity.com/paper/2505.22067/) (2025) - [SG-CADVLM: A Context-Aware Decoding Powered Vision Language Model for Safety-Critical Scenario Generation](https://mlsanity.com/paper/2601.18442/) (2026) - [Risk-Controllable Multi-View Diffusion for Driving Scenario Generation](https://mlsanity.com/paper/2603.11534/) (2026) - [Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving](https://mlsanity.com/paper/2607.14387/) (2026) - [AdvSerial: Physical Adversarial Attacks on Infrastructure-mounted Pedestrian Detectors via Semantic Feature Suppression](https://mlsanity.com/paper/2607.17069/) (2026) - [PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving](https://mlsanity.com/paper/2609.08965/) (2026) - [GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic](https://mlsanity.com/paper/2605.10386/) (2026) - [From Driving Videos to Simulatable Scenarios](https://mlsanity.com/paper/2606.21993/) (2026) - [Language-Structured Relational Q-Learning for Threat-Aware Control in Safety-Critical Driving](https://mlsanity.com/paper/2608.11498/) (2026) ## Abstract (arXiv) 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.