# Scenic: A Language for Scenario Specification and Scene Generation

arXiv: https://arxiv.org/abs/1809.09310 · HTML: https://mlsanity.com/paper/1809.09310/

## Key facts

- Cited by 83 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2018.

## Built on by (most cited first, 30 of 83)

- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [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)
- [Deep Surrogate Assisted Generation of Environments](https://mlsanity.com/paper/2206.04199/) (2022)
- [Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis](https://mlsanity.com/paper/2506.11526/) (2025)
- [Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems](https://mlsanity.com/paper/2405.20774/) (2024)
- [Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving](https://mlsanity.com/paper/2311.11056/) (2023)
- [Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles](https://mlsanity.com/paper/2103.07403/) (2021)
- [Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems](https://mlsanity.com/paper/2108.11800/) (2021)
- [Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles](https://mlsanity.com/paper/2409.06450/) (2024)
- [Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test](https://mlsanity.com/paper/2503.02911/) (2025)
- [A Survey of Attacks on Large Language Models](https://mlsanity.com/paper/2505.12567/) (2025)
- [Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs](https://mlsanity.com/paper/2401.06379/) (2024)
- [Neural Network Verification is a Programming Language Challenge](https://mlsanity.com/paper/2501.05867/) (2025)
- [CHAI: Command Hijacking against embodied AI](https://mlsanity.com/paper/2510.00181/) (2025)
- [ScenicNL: Generating Probabilistic Scenario Programs from Natural Language](https://mlsanity.com/paper/2405.03709/) (2024)
- [Four Principles for Physically Interpretable World Models](https://mlsanity.com/paper/2503.02143/) (2025)
- [Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks](https://mlsanity.com/paper/2304.05727/) (2023)
- [Parallel and Multi-Objective Falsification with Scenic and VerifAI](https://mlsanity.com/paper/2107.04164/) (2021)
- [ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA](https://mlsanity.com/paper/2208.06309/) (2022)
- [Surrogate Assisted Generation of Human-Robot Interaction Scenarios](https://mlsanity.com/paper/2304.13787/) (2023)
- [Text-to-Drive: Diverse Driving Behavior Synthesis via Large Language Models](https://mlsanity.com/paper/2406.04300/) (2024)
- [LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation](https://mlsanity.com/paper/2411.00773/) (2024)
- [From Dashcam Videos to Driving Simulations: Stress Testing Automated Vehicles against Rare Events](https://mlsanity.com/paper/2411.16027/) (2024)
- [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)
- [Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification](https://mlsanity.com/paper/2205.10906/) (2022)
- [Enhancing LLM-based Autonomous Driving Agents to Mitigate Perception Attacks](https://mlsanity.com/paper/2409.14488/) (2024)
- [Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner](https://mlsanity.com/paper/2412.18086/) (2024)
- [Addressing the IEEE AV Test Challenge with Scenic and VerifAI](https://mlsanity.com/paper/2108.13796/) (2021)

## Abstract (arXiv)

We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle 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 and sampling these to generate specialized training and test sets. More generally, such languages can be used for cyber-physical systems and robotics 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 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. 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.