用因果概率模型让自动驾驶仿真更真实，暴露感知缺陷带来的安全隐患。
A Causal Probabilistic Framework for Perception-Informed Closed-Loop Simulation of Autonomous Driving
- 将因果概率模型融入标准仿真流程，模拟真实环境下的感知错误。
- 在雾、雨等条件下揭示检测丢失、定位偏差等关键感知故障。
- 适合关注SOTIF安全验证的自动驾驶研发与测试团队。
软件在环（SIL）仿真已成为现代汽车安全功能验证的核心手段。然而，当前多数框架采用理想化感知，忽略了感知算法的功能缺陷，导致安全评估过于乐观。本文提出一种感知驱动的SIL测试方法，弥合了真实世界感知行为与理想化仿真之间的差距。通过将因果概率模型集成至标准化场景化仿真工具链中，该方法可系统性地注入现实感知误差，如检测丢失、尺寸误判和位置偏移，这些误差源自雾、雨及物体融合等物理触发条件。在标准仿真环境中评估这些“故障”后发现，感知信息驱动的测试能揭示理想仿真环境无法捕捉的潜在运行风险，为满足ISO 21448 SOTIF规范提供了可扩展的验证路径。
原文摘要 · Abstract (English)
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.
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