Scenic: A Language for Scenario Specification and Data Generation
This addresses the need for better robustness and debugging in cyber-physical systems, such as autonomous vehicles, by providing a domain-specific tool for scenario specification and data generation.
The authors tackled the problem of training and testing machine learning systems, like autonomous cars, to be robust to rare events by proposing Scenic, a probabilistic programming language for specifying scenario distributions. They applied Scenic to improve a car detection neural network's performance beyond state-of-the-art synthetic data generation methods.
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.