# Out of Sight But Not Out of Mind: An Answer Set Programming Based Online Abduction Framework for Visual Sensemaking in Autonomous Driving

## TL;DR

This paper presents a modular answer set programming framework that integrates deep learning visual computing for online visual sensemaking in autonomous driving, emphasizing explainability and human-centered understanding.

## Contribution

It introduces a formalized, fully implemented method combining vision and semantics for autonomous driving perception within a hybrid architecture.

## Key findings

- Effective integration of deep learning and ASP for visual sensemaking
- Demonstrated on benchmarks KITTIMOD and MOT
- Enhanced explainability and human-centered reasoning in autonomous driving

## Abstract

We demonstrate the need and potential of systematically integrated vision and semantics} solutions for visual sensemaking (in the backdrop of autonomous driving). A general method for online visual sensemaking using answer set programming is systematically formalised and fully implemented. The method integrates state of the art in (deep learning based) visual computing, and is developed as a modular framework usable within hybrid architectures for perception & control. We evaluate and demo with community established benchmarks KITTIMOD and MOT. As use-case, we focus on the significance of human-centred visual sensemaking ---e.g., semantic representation and explainability, question-answering, commonsense interpolation--- in safety-critical autonomous driving situations.