论文标题
MTBF模型 - 从感知错误到车辆级别的故障
MTBF Model for AVs -- From Perception Errors to Vehicle-Level Failures
论文作者
论文摘要
自动车辆（AVS）的开发正在迅速发展，并且在全球范围内部署了第一家Robotaxi服务。但是，要获得大规模部署的权限认证，制造商需要证明其AV比人类驾驶员更安全。反过来，这需要估计和建模AV的碰撞率（故障率），考虑了所有可能的错误并考虑到驾驶情况。换句话说，对AVS的失败（MTBF）模型之间对全面的平均时间有强烈的需求。在本文中，我们将引入这样的通用且可扩展的模型，该模型在感知系统中的错误与车辆级别的故障（碰撞）之间创建链接。使用此模型，我们能够根据所需的车辆级MTBF来得出对感知质量的要求，反之亦然，以获取MTBF值，并且给定特定的任务概况和感知质量。
The development of Automated Vehicles (AVs) is progressing quickly and the first robotaxi services are being deployed worldwide. However, to receive authority certification for mass deployment, manufactures need to justify that their AVs operate safer than human drivers. This in turn creates the need to estimate and model the collision rate (failure rate) of an AV taking all possible errors and driving situations into account. In other words, there is the strong demand for comprehensive Mean Time Between Failure (MTBF) models for AVs. In this paper, we will introduce such a generic and scalable model that creates a link between errors in the perception system to vehicle-level failures (collisions). Using this model, we are able to derive requirements for the perception quality based on the desired vehicle-level MTBF or vice versa to obtain an MTBF value given a certain mission profile and perception quality.
