News

Simula Research Laboratory recently contributed to IEEE IV 2025 with two papers presented during the AI4CCAM-sponsored workshop entitled “Advancing AD in Highly Interactive Scenarios through Behaviour Prediction, Trustworthy AI, and Remote Operations“, which were also accepted for presentation as posters in the main Conference. These papers both relate to scene understanding using qualitative spatio-temporal representations:

Automatic Cause Determination in Road Scene Understanding Using Qualitative Reasoning and Four-Valued Logic

Road scene understanding in automated driving (AD) aims to build a comprehensive analysis of video sequences taken on the road by embedded or fixed cameras (e.g., mounted on vertical road signals). One goal is to identify the relevant actors in the scene and another goal is to determine the causes that have triggered a specific action of the ego car (i.e., stop, slow down, turn left, etc.). In a complex urban environment, these causes can be multiple, confusing, possibly contradictory to other causes and not easily expressible using simplistic reasoning. Still, providing accurate automatic cause determination supports a) user acceptance by providing appropriate explanations to the car passengers and road users; b) increased road safety by providing detailed road scene understanding to traffic. In this paper, AI4CCAM proposes using spatiotemporal reasoning and Belnap’s four-valued logic to formulate complex causes of AD action in a road scene.

Explainable Scene Understanding with Qualitative Representations and Graph Neural Networks

This paper investigates the integration of graph neural networks (GNNs) with Qualitative Explainable Graphs (QXGs) for scene understanding in automated driving. Scene understanding is the basis for any further reactive or proactive decision-making. Scene understanding and related reasoning is inherently an explanation task: why is another traffic participant doing something, what or who caused their actions? While previous work demonstrated QXGs’ effectiveness using shallow machine learning models, these approaches were limited to analysing single relation chains between object pairs, disregarding the broader scene context. AI4CCAM proposes a novel GNN architecture that processes entire graph structures to identify relevant objects in traffic scenes.

Read and download the papers!