论文标题
高级融合感知系统的基于合理的故障检测方法
A Plausibility-based Fault Detection Method for High-level Fusion Perception Systems
论文作者
论文摘要
值得信赖的环境感知是安全部署自动驾驶汽车或智能机器人等自动化代理的基本基础。问题仍然存在，众所周知，这种信任在存在系统的缺陷的情况下很难保证，例如由机器学习功能引起的不可追踪错误。解决此问题的一种无需对感知过程做出相当具体假设的方法是检查。与人类直觉的推理类似，复杂的黑盒程序的最终结果是根据对象行为的给定期望来验证的。在本文中，我们应用和评估了协作，传感器 - 理由的合理性检查是一种从其统计指纹中检测经验理解错误的平均值。我们的真正用例是下一代自动驾驶，该驾驶使用路边传感器基础设施进行感知增强，在这里以德国高速公路和城市交叉路口的测试场景为代表。 plausibilization分析自然集成在对象融合过程中，并有助于诊断分布式感应系统中已知甚至可能未知的故障。
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.
