  We demonstrate the need and potential of systematically integrated vision and
semantics solutions for visual sensemaking in the backdrop of autonomous
driving. A general neurosymbolic method for online visual sensemaking using
answer set programming (ASP) is systematically formalised and fully
implemented. The method integrates state of the art in visual computing, and is
developed as a modular framework that is generally usable within hybrid
architectures for realtime perception and control. We evaluate and demonstrate
with community established benchmarks KITTIMOD, MOT-2017, and MOT-2020. As
use-case, we focus on the significance of human-centred visual sensemaking --
e.g., involving semantic representation and explainability, question-answering,
commonsense interpolation -- in safety-critical autonomous driving situations.
The developed neurosymbolic framework is domain-independent, with the case of
autonomous driving designed to serve as an exemplar for online visual
sensemaking in diverse cognitive interaction settings in the backdrop of select
human-centred AI technology design considerations.
  Keywords: Cognitive Vision, Deep Semantics, Declarative Spatial Reasoning,
Knowledge Representation and Reasoning, Commonsense Reasoning, Visual
Abduction, Answer Set Programming, Autonomous Driving, Human-Centred Computing
and Design, Standardisation in Driving Technology, Spatial Cognition and AI.
