# Scenic: A Language for Scenario Specification and Data Generation

arXiv: https://arxiv.org/abs/2010.06580 · HTML: https://mlsanity.com/paper/2010.06580/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 30 of 40)

- [KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients](https://mlsanity.com/paper/2204.13683/) (2022)
- [Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems](https://mlsanity.com/paper/2405.06624/) (2024)
- [ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles](https://mlsanity.com/paper/2405.14062/) (2024)
- [Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model](https://mlsanity.com/paper/2409.09575/) (2024)
- [Learning to Drive via Asymmetric Self-Play](https://mlsanity.com/paper/2409.18218/) (2024)
- [ScenicNL: Generating Probabilistic Scenario Programs from Natural Language](https://mlsanity.com/paper/2405.03709/) (2024)
- [Parallel and Multi-Objective Falsification with Scenic and VerifAI](https://mlsanity.com/paper/2107.04164/) (2021)
- [LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation](https://mlsanity.com/paper/2411.00773/) (2024)
- [Dialogue-based generation of self-driving simulation scenarios using Large Language Models](https://mlsanity.com/paper/2310.17372/) (2023)
- [Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments](https://mlsanity.com/paper/2106.10365/) (2021)
- [A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation](https://mlsanity.com/paper/2110.14870/) (2021)
- [From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving](https://mlsanity.com/paper/2305.13960/) (2023)
- [Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages](https://mlsanity.com/paper/2406.03636/) (2024)
- [Addressing the IEEE AV Test Challenge with Scenic and VerifAI](https://mlsanity.com/paper/2108.13796/) (2021)
- [Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach](https://mlsanity.com/paper/2311.07377/) (2023)
- [Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles](https://mlsanity.com/paper/2508.14527/) (2025)
- [Conversational Code Generation: a Case Study of Designing a Dialogue System for Generating Driving Scenarios for Testing Autonomous Vehicles](https://mlsanity.com/paper/2410.09829/) (2024)
- [Querying Labelled Data with Scenario Programs for Sim-to-Real Validation](https://mlsanity.com/paper/2112.00206/) (2021)
- [Bridging Data-Driven and Knowledge-Driven Approaches for Safety-Critical Scenario Generation in Automated Vehicle Validation](https://mlsanity.com/paper/2311.10937/) (2023)
- [Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving](https://mlsanity.com/paper/2403.17805/) (2024)
- [ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving](https://mlsanity.com/paper/2209.10078/) (2022)
- [Interactive Program Synthesis for Modeling Collaborative Physical Activities from Narrated Demonstrations](https://mlsanity.com/paper/2509.24250/) (2025)
- [ScenicProver: A Framework for Compositional Probabilistic Verification of Learning-Enabled Systems](https://mlsanity.com/paper/2511.02164/) (2025)
- [Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations](https://mlsanity.com/paper/2308.03276/) (2023)
- [Automatic Generation of Scenarios for System-level Simulation-based Verification of Autonomous Driving Systems](https://mlsanity.com/paper/2311.09784/) (2023)
- [Natural-language-driven Simulation Benchmark and Copilot for Efficient Production of Object Interactions in Virtual Road Scenes](https://mlsanity.com/paper/2312.04008/) (2023)
- [Outline of an Independent Systematic Blackbox Test for ML-based Systems](https://mlsanity.com/paper/2401.17062/) (2024)
- [David vs. Goliath: A comparative study of different-sized LLMs for code generation in the domain of automotive scenario generation](https://mlsanity.com/paper/2510.14115/) (2025)
- [Controllable risk scenario generation from human crash data for autonomous vehicle testing](https://mlsanity.com/paper/2512.07874/) (2025)
- [DriverGaze360: OmniDirectional Driver Attention with Object-Level Guidance](https://mlsanity.com/paper/2512.14266/) (2025)

## Abstract (arXiv)

We propose a new probabilistic programming language for the design and analysis of cyber-physical systems, especially those based on machine learning. Specifically, we consider the problems of training a system to be robust to rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs, then sampling these to generate specialized training and test data. More generally, such languages can be used to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems like autonomous cars and robots, whose environment at any point in time is a 'scene', a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in a case study on a convolutional neural network designed to detect cars in road images, improving its performance beyond that achieved by state-of-the-art synthetic data generation methods.