UIRAM: An intention-uncertainty-based risk assessment framework for interactive traffic scenarios

The growing heterogeneity of driving behaviors makes accurate identification and quantitative assessment of imminent collisions a cornerstone of safety assurance. Yet existing risk detection and scoring methods often generalize poorly across complex interactive scenarios and insufficiently account for uncertainty in surrounding agents' future behavior. To address this issue, we propose UIRAM, an intention-uncertainty-based risk assessment framework for interactive traffic participants. UIRAM first rapidly selects collision-relevant candidates using a simplified two-dimensional Gaussian conflict domain, then predicts for each participant an uncertainty-aware Gaussian trajectory distribution through an intention prediction network that fuses environmental context and interaction dynamics. Based on these predictions, the framework estimates collision probability and further combines it with consequence severity to derive an overall risk score, using the maximum per-agent risk as the scenario-level indicator. Experiments on the FLUID dataset show that UIRAM achieves up to a 0.22-point AUC improvement over representative baselines while completing per-vehicle uncertainty prediction and risk computation within 0.14s. Driver-in-the-loop simulations further demonstrate strong trend-level consistency between UIRAM's risk outputs and drivers' subjective risk perception.