Factor graph scene distributions for automotive safety analysis

Automotive safety validation requires evaluation on a statistically representative set of roadway configurations and scene geometries. Scenes must be sampled from a statistical model representative of what actually occurs on roadways. This paper introduces a methodology for realistic scene model construction based on factor graphs that can be applied to arbitrary road geometries. Parameter learning for factor graphs is known to be convex. Experiments show that the proposed method is superior to the state of the art.