Plane & Sample: Maximizing Information about Autonomous Vehicle Performance Using Submodular Optimization
Authors: Anne Collin, Amitai Y. Bin-Nun, Radboud Duintjer Tebbens
Published: June 15, 2021
Summary: In this paper, we reformulate the scenario sampling problem across ODDs and functionalities as a submodular optimization problem. To do so, we abstract AV performance as a Bayesian Hierarchical Model, which we use to infer information gained by revealing performance in new scenarios. We propose the information gain as a measure of scenario relevance and evaluation progress. Furthermore, we leverage the submodularity, or diminishing returns, property of the information gain not only to find a near-optimal scenario set, but also to propose a stopping criterion for an AV performance evaluation campaign.
