  While object detection modules are essential functionalities for any
autonomous vehicle, the performance of such modules that are implemented using
deep neural networks can be, in many cases, unreliable. In this paper, we
develop abstraction-based monitoring as a logical framework for filtering
potentially erroneous detection results. Concretely, we consider two types of
abstraction, namely data-label abstraction and post-algorithm abstraction.
Operated on the training dataset, the construction of data-label abstraction
iterates each input, aggregates region-wise information over its associated
labels, and stores the vector under a finite history length. Post-algorithm
abstraction builds an abstract transformer for the tracking algorithm. Elements
being associated together by the abstract transformer can be checked against
consistency over their original values. We have implemented the overall
framework to a research prototype and validated it using publicly available
object detection datasets.
