aiqbench analysis
This paper introduces a causal probabilistic framework for Software-in-the-loop (SIL) simulation that injects realistic perception errors, such as detection loss and sizing inaccuracies, to address the over-optimism of idealized testing environments. By aligning ground-truth simulations with actual sensor behavior under adverse conditions like fog or rain, the method provides a scalable pathway for validating SOTIF (ISO 21448) requirements in both ADAS and ADS. The work is significant for engineering teams seeking to uncover latent operational risks that standard simulation toolchains currently miss.
A Causal Probabilistic Framework for Perception-Informed Closed-Loop Simulation of Autonomous Driving
arXiv:2606.07186v1 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. We present a framework for incorporating causal probabilistic models into standardized, scenario-based simulation toolchains, applicable to both Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). Our approach enables the systematic injection of realistic perception errors, such as loss of detection, sizing inaccuracies, and positioning offsets, derived from physical triggering conditions like fog, rain, and object-merging scenarios. By evaluating these ``faults'' within a standardized simulation environment, we demonstrate that perception-informed testing reveals latent operational risks that ideal SIL environments fail to capture, providing a scalable pathway for SOTIF (ISO 21448) validation.
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. We present a framework for incorporating causal probabilistic models into standardized, scenario-based simulation toolchains, applicable to both Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). Our approach enables the systematic injection of realistic perception errors, such as loss of detection, sizing inaccuracies, and positioning offsets, derived from physical triggering conditions like fog, rain, and object-merging scenarios. By evaluating these ``faults'' within a standardized simulation environment, we demonstrate that perception-informed testing reveals latent operational risks that ideal SIL environments fail to capture, providing a scalable pathway for SOTIF (ISO 21448) validation.
Robotics (cs.RO); Software Engineering (cs.SE)
arXiv:2606.07186 [cs.RO]
(or arXiv:2606.07186v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.07186 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 165 words