Scenic: A Language for Scenario Specification and Scene Generation

Understand

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.