Real-Time Incremental Explanations for Object Detectors in Autonomous Driving

Abstract

Object detectors are widely used in safety-critical real-time applications such as autonomous driving. Explainability is especially important for safety-critical applications, and due to the variety of object detectors and their often proprietary nature, black-box explainability tools are needed. However, existing black-box explainability tools for AI models rely on multiple model calls, rendering them impractical for real-time use. In this paper, we introduce IncX, an algorithm and a tool for real-time black-box explainability for object detectors. The algorithm is based on linear transformations of saliency maps, producing sufficient explanations. We evaluate our implementation on four widely used video datasets of autonomous driving and demonstrate that IncX's explanations are comparable in quality to the state-of-the-art and are computed two orders of magnitude faster than the state-of-the-art, making them usable in real time.