Causal Probabilistic Framework for Perception-Informed
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A Causal Probabilistic Framework for Perception-Informed Closed-Loop Simulation of Autonomous Driving
Announce Type: new Abstract: Software-in-the-loop (SIL) simulation is a cornerstone for the validation of modern automotive safety functions. However, many current frameworks utilize ideal sensing, which bypasses the functional insufficiencies of perception algorithms, leading to over-optimistic safety assessments. This paper proposes a perception-informed SIL testing methodology that bridges the gap between ground-truth simulation and real-world perception behavior.