  Falsification has emerged as an important tool for simulation-based
verification of autonomous systems. In this paper, we present extensions to the
Scenic scenario specification language and VerifAI toolkit that improve the
scalability of sampling-based falsification methods by using parallelism and
extend falsification to multi-objective specifications. We first present a
parallelized framework that is interfaced with both the simulation and sampling
capabilities of Scenic and the falsification capabilities of VerifAI, reducing
the execution time bottleneck inherently present in simulation-based testing.
We then present an extension of VerifAI's falsification algorithms to support
multi-objective optimization during sampling, using the concept of rulebooks to
specify a preference ordering over multiple metrics that can be used to guide
the counterexample search process. Lastly, we evaluate the benefits of these
extensions with a comprehensive set of benchmarks written in the Scenic
language.
