# Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations

arXiv: https://arxiv.org/abs/2403.09668 · HTML: https://mlsanity.com/paper/2403.09668/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Explanations in Autonomous Driving: A Survey](https://mlsanity.com/paper/2103.05154/) (2021)
- [Commonsense Visual Sensemaking for Autonomous Driving: On Generalised Neurosymbolic Online Abduction Integrating Vision and Semantics](https://mlsanity.com/paper/2012.14359/) (2020)
- [Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving](https://mlsanity.com/paper/2205.01532/) (2022)
- [Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation](https://mlsanity.com/paper/2308.12755/) (2023)

## Built on by (most cited first, 1 of 1)

- [Explainable Scene Understanding with Qualitative Representations and Graph Neural Networks](https://mlsanity.com/paper/2504.12817/) (2025)

## Abstract (arXiv)

We present the Qualitative Explainable Graph (QXG): a unified symbolic and qualitative representation for scene understanding in urban mobility. QXG enables the interpretation of an automated vehicle's environment using sensor data and machine learning models. It leverages spatio-temporal graphs and qualitative constraints to extract scene semantics from raw sensor inputs, such as LiDAR and camera data, offering an intelligible scene model. Crucially, QXG can be incrementally constructed in real-time, making it a versatile tool for in-vehicle explanations and real-time decision-making across various sensor types. Our research showcases the transformative potential of QXG, particularly in the context of automated driving, where it elucidates decision rationales by linking the graph with vehicle actions. These explanations serve diverse purposes, from informing passengers and alerting vulnerable road users (VRUs) to enabling post-analysis of prior behaviours.