Aprendizaje de interacciones espacio-temporales consciente de la incertidumbre para la evolución del riesgo...

Quantifying real-time conflict risk and revealing its evolution are of great importance for enhancing vehicle active safety. Recent studies estimate dynamic risk via conflict probability, yet annotation still relies on threshold based static views and uncertainty is only partially modeled, which limits the assessment of a model's ability to learn conflict evolution. Addressing this gap, we posit a prior hypothesis of increasing pre-conflict risk and develop a risk quantification model that integrates driver control inputs and multi vehicle spatiotemporal interactions with explicit uncertainty outputs. The model is evaluated for accuracy and stability of risk perception, parameter sensitivity, and capacity for pattern learning. Experiments show that, relative to Time To Collision (TTC), Deceleration Rate to Avoid a Crash (DRAC), Proportion of Stopping Distance (PSD), Anticipated Collision Time (ACT),and Emergency Index (EI), the proposed model achieves stronger risk discrimination. On the test set of 15 conflict events used in this study, the proposed model detects elevated conflict risk on average 1.15 s before the conflict point. In four representative scenarios, including car following, ego lane change, unobstructed cut in and cut in under occluded view, the proposed model yields a lower false alarm rate than TTC and, on average, perceives rising conflict risk 1.44 s before the conflict point. Uncertainty analysis indicates lower uncertainty during the rising risk phase, enabling reliable capture of risk evolution. Sensitivity results support the expressiveness of the proposed hypothesis and reveal a common regularity across scenarios, where risk begins to increase approximately 4-6 s before conflict. The results establish a pre conflict risk modeling paradigm that jointly estimates risk and its confidence, supports calibration and transfer across scenarios, and provides an operational basis for proactive safety assessment.