To receive authoritative authentication of large scale deployment of autonomous vehicles (AVs), manufacturers need to prove their AVs are safer than human drivers. This in turn results in the need to estimate and model the collision rate (failure rate) of the AV taking into account all possible errors and driving situations. In other words, AV has a strong need for a comprehensive mean time-to-failure (MTBF) model. This disclosure describes such a generic and scalable model that creates a connection between errors in a perceptual system and vehicle level faults (collisions). Using this model, requirements for perceived quality may then be derived based on a desired vehicle level MTBF, or vice versa, to obtain MTBF values given a particular task profile and perceived quality.

    为了接收大规模部署自主车辆(AV)的权威认证,制造商需要证明他们的AV比人类驾驶员更安全。这继而产生了考虑所有可能的错误和驾驶情况来对AV的碰撞率(故障率)进行估计和建模的需要。换句话说,AV对全面的平均故障间隔时间(MTBF)模型存在强烈需求。本公开描述了此类通用且可扩展的模型,所述模型在感知系统中的错误与车辆级别故障(碰撞)之间创建了联系。使用此模型,然后可基于期望的车辆级别MTBF导出针对感知质量的要求,或反之亦然,以获得给定特定任务概况和感知质量的MTBF值。


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    Title :

    Application of average time-to-failure (MTBF) model for autonomous vehicles


    Additional title:

    平均故障间隔时间(MTBF)模型针对自主车辆的应用


    Contributors:

    Publication date :

    2024-09-20


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion



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