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.
@article{arxiv.1906.00107,
title = {Out of Sight But Not Out of Mind: An Answer Set Programming Based Online Abduction Framework for Visual Sensemaking in Autonomous Driving},
author = {Jakob Suchan and Mehul Bhatt and Srikrishna Varadarajan},
journal= {arXiv preprint arXiv:1906.00107},
year = {2019}
}
Comments
IJCAI 2019: the 28th International Joint Conference on Artificial Intelligence (IJCAI) 2019, August 10 - 16, Macao. (Preprint / to appear)