Trustworthy environment perception is the fundamental basis for the safe deployment of automated agents such as self-driving vehicles or intelligent robots. The problem remains that such trust is notoriously difficult to guarantee in the presence of systematic faults, e.g., non-traceable errors caused by machine learning functions. One way to tackle this issue without making rather specific assumptions about the perception process is plausibility checking. Similar to the reasoning of human intuition, the final outcome of a complex black-box procedure is verified against given expectations of an object’s behavior. In this article, we apply and evaluate collaborative, sensor-generic plausibility checking as a mean to detect empirical perception faults from their statistical fingerprints. Our real use case is next-generation automated driving that uses a roadside sensor infrastructure for perception augmentation, represented here by test scenarios at a German highway and a city intersection. The plausibilization analysis is integrated naturally in the object fusion process, and helps to diagnose known and possibly yet unknown faults in distributed sensing systems.

中文翻译：

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基于融合的高级融合感知系统故障检测方法

可信赖的环境感知是安全部署自动代理（例如自动驾驶车辆或智能机器人）的基本基础。问题仍然是，在存在系统性故障（例如，由机器学习功能导致的不可追溯的错误）的情况下，很难保证这种信任。在不对感知过程做出特定假设的情况下解决此问题的一种方法是合理性检查。与人类直觉的推理类似，在给定的对象行为预期的情况下，验证了复杂的黑盒程序的最终结果。在本文中，我们应用并评估了协作的，传感器通用的似真性检查，以此作为从其统计指纹中检测出经验感知错误的手段。我们真正的用例是使用路边传感器基础设施进行感知增强的下一代自动驾驶，此处以德国高速公路和城市交叉路口的测试场景为代表。可行性分析自然地集成在对象融合过程中，并有助于诊断分布式传感系统中的已知和可能未知的故障。