Scenic: A Language for Scenario Specification and Scene Generation (1809.09310v2)

Abstract: 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.

• The paper introduces Scenic, a language framework for specifying complex scenarios with both hard and soft constraints.
• It develops domain-specific sampling techniques that efficiently generate valid scenes by leveraging geometric constraints and simulator integration.
• Empirical evaluations demonstrate that synthetic scenarios generated with Scenic boost ML model performance, particularly for rare and corner-case events.

Analyzing Scenic: A Language for Scenario Specification and Scene Generation

The paper “Scenic: A Language for Scenario Specification and Scene Generation” introduces Scenic, a domain-specific probabilistic programming language designed specifically to define scenarios for the generation of synthetic data. The research focuses on addressing challenges related to training, testing, and debugging perceptual systems utilized in machine learning, particularly those associated with cyber-physical systems like autonomous vehicles.

Core Contributions

Scenic allows users to specify complex scenes as probabilistic distributions over configurations of objects within a real or simulated environment. Using a declarative syntax, users can define both hard constraints (which must always be satisfied) and soft constraints (which should hold with certain probability). This ability to generate and control synthetic data distributions directly addresses the bottleneck in acquiring training data from rare or specific real-world events.

The primary contributions of the paper include:

1. Language Framework: By designing Scenic, the authors provide a flexible tool to specify probabilistic models of environments. This framework is characterized by a concise representation of geometric and logical constraints, making it intuitive to understand and modify.
2. Sampling Techniques: The work innovates on domain-specific sampling algorithms, which leverage the geometric nature of most scenarios. By integrating with existing simulators, the language can efficiently generate valid scenarios that satisfy specified constraints.
3. Experimental Evaluation: A significant case paper with a convolutional neural network for object detection demonstrates that data generated by Scenic can enhance model accuracy. The authors effectively show performance improvements over traditional synthetic data generation methods.

Numerical Results and Claims

The paper presents empirical results substantiating the efficacy of using Scenic for synthetic data generation. For instance, the performance assessment presents quantifiable improvements in the classification precision and recall of trained models, particularly in rare or corner-case scenarios. The integration of Scenic into a neural network testing pipeline yields noteworthy advances beyond state-of-the-art benchmarks, thus validating its practical utility.

Implications and Future Directions

The introduction of Scenic carries substantial implications for the design and analysis of ML-based perception systems. It provides a robust methodology to supplement real-world data with synthetic equivalents that are realistically modeled, thus potentially reducing incidents of AI failure in edge cases. Scenic facilitates a comprehensive approach to debugging—identifying failure patterns and retraining models to address them.

Looking forward, Scenic offers several avenues for further exploration. Expanding the language to handle the specification and generation of 3D environments, or dynamic and temporal scenarios, would significantly widen its applicability. Moreover, extending its integration with diverse simulators could spur developments in numerous domains, ranging from robotics to interactive simulation environments.

In conclusion, Scenic emerges as a critical tool in the field of machine learning and synthetic data generation. Its language design and domain-specific optimizations not only promise increased robustness in perception systems but also enhance efficiency in simulator-driven testing and training paradigms.