A Risk Reducing Fleet Monitor for Automated Vehicles Based on Extreme Value Theory
The verification and validation of automated vehicles (AVs) is a challenging problem. There are currently no verification and validation methods that can guarantee the absence of unreasonable risk. Therefore, monitoring methods are recommended to measure the residual risk caused by the AVs in the field. This paper proposes a proactive fleet monitoring approach for AVs based on Extreme Value Theory (EVT) to reduce the accident risk during first deployment and software updates. By performing sequential statistical tests on threat metrics measured in an AV fleet, the monitor is used to quickly identify and abort operations if the AVs do not meet the required level of safety. To evaluate the proposed monitoring approach, it is studied in a fictive deployment case using two different threat metrics, one predictive and one retrospective. The evaluation showed that a significant risk reduction is achievable when using the EVT fleet monitor compared to reactive fleet monitoring. Using the predictive threat metric reduces the risk and number of accidents by aborting operations unless the deployed AVs have substantially higher MTBF than required. Comparatively, the introduced retrospective threat metric is a more balanced alternative that can reduce risk without stopping operations when the required MTBF is met. In summation, EVT fleet monitoring appears to be a promising method that can be used to reduce the risk of accidents caused by sub-performing AV driving functions by aborting operations before accidents are caused.
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ORCID of Submitting Author0000-0003-1670-6078
Submitting Author's InstitutionZenseact AB
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