# Commonsense Visual Sensemaking for Autonomous Driving: On Generalised Neurosymbolic Online Abduction Integrating Vision and Semantics

arXiv: https://arxiv.org/abs/2012.14359 · HTML: https://mlsanity.com/paper/2012.14359/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2020.

## Built on by (most cited first, 15 of 15)

- [Neuro-Symbolic Learning: Principles and Applications in Ophthalmology](https://mlsanity.com/paper/2208.00374/) (2022)
- [Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap](https://mlsanity.com/paper/2404.00019/) (2024)
- [Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation](https://mlsanity.com/paper/2308.12755/) (2023)
- [Assessing Drivers' Situation Awareness in Semi-Autonomous Vehicles: ASP based Characterisations of Driving Dynamics for Modelling Scene Interpretation and Projection](https://mlsanity.com/paper/2308.15895/) (2023)
- [Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations](https://mlsanity.com/paper/2403.09668/) (2024)
- [A Safety Assurable Human-Inspired Perception Architecture](https://mlsanity.com/paper/2205.07862/) (2022)
- [Neuro-Symbolic Spatio-Temporal Reasoning](https://mlsanity.com/paper/2211.15566/) (2022)
- [One-shot Machine Teaching: Cost Very Few Examples to Converge Faster](https://mlsanity.com/paper/2212.06416/) (2022)
- [Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations](https://mlsanity.com/paper/2403.16908/) (2024)
- [Commonsense Reasoning-Aided Autonomous Vehicle Systems](https://mlsanity.com/paper/2502.09233/) (2025)
- [Correcting Autonomous Driving Object Detection Misclassifications with Automated Commonsense Reasoning](https://mlsanity.com/paper/2601.04271/) (2026)
- [Visual Commonsense Driven Knowledge Refinements for Scene Graph Generation](https://mlsanity.com/paper/2606.06369/) (2026)
- [Reasonable Motion: A General ASP Foundation for Environment Constrained Movement Trajectory Computation](https://mlsanity.com/paper/2606.25626/) (2026)
- [Answer Set Programming Energised! End-to-End Neurosymbolic Reasoning and Learning with ASP and Energy Based Models](https://mlsanity.com/paper/2607.08136/) (2026)
- [A Human-Factors Guided Cognitive Model of Visuospatial Complexity in Embodied Active Vision](https://mlsanity.com/paper/2608.23572/) (2026)

## Abstract (arXiv)

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.