面向感知信息闭环仿真的自动驾驶因果概率框架
A Causal Probabilistic Framework for Perception-Informed Closed-Loop Simulation of Autonomous Driving
AI总结：
提出一种因果概率模型框架，将感知误差注入标准仿真环境，揭示理想SIL无法捕获的潜在风险，为SOTIF验证提供可扩展路径。
摘要：
软件在环（SIL）仿真是现代汽车安全功能验证的基石。然而，许多当前框架采用理想感知，绕过了感知算法的功能不足，导致过于乐观的安全评估。本文提出一种感知信息SIL测试方法，弥合了地面实况仿真与真实世界感知行为之间的差距。我们提出了一个将因果概率模型纳入标准化、基于场景的仿真工具链的框架，适用于高级驾驶辅助系统（ADAS）和自动驾驶系统（ADS）。我们的方法能够系统性地注入由物理触发条件（如雾、雨和物体合并场景）导出的真实感知误差，例如检测丢失、尺寸不准确和定位偏移。通过在标准化仿真环境中评估这些“故障”，我们证明了感知信息测试揭示了理想SIL环境无法捕获的潜在操作风险，为SOTIF（ISO 21448）验证提供了可扩展的途径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
