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<p>This paper proposes a proactive fleet monitoring approach for automated vehicles (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 can be 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. Moreover, using a predictive threat metric reduces the risk of accidents dramatically. However, it has the drawback of frequently aborting operations unless the systems are significantly better than required. On the other hand, a retrospective threat metric was a more balanced alternative that could substantially reduce risk without being overly conservative. In summation, the EVT fleet monitoring is a promising complementary approach to traditional validation to minimize the risk and abort operations of sub-performing new deployments well before any accidents are caused.</p>

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<p>This paper proposes a proactive fleet monitoring approach for automated vehicles (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 can be 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. Moreover, using a predictive threat metric reduces the risk of accidents dramatically. However, it has the drawback of frequently aborting operations unless the systems are significantly better than required. On the other hand, a retrospective threat metric was a more balanced alternative that could substantially reduce risk without being overly conservative. In summation, the EVT fleet monitoring is a promising complementary approach to traditional validation to minimize the risk and abort operations of sub-performing new deployments well before any accidents are caused.</p>

<p>The verification and validation of automated vehicles (AVs) is known to be a challenging problem. There are currently no verification and validation methods that can guarantee completeness and a high level of safety without extensive field tests. Therefore, monitoring methods are recommended to ensure the absence of unreasonable 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. Moreover, using a predictive threat metric reduces the risk of accidents dramatically. However, it has the drawback of frequently aborting operations unless the systems are significantly better than required. On the other hand, a retrospective threat metric is found to be a more balanced alternative that could substantially reduce risk without being overly conservative. In summation, the EVT fleet monitoring is a promising complementary approach to traditional validation to minimize the risk and abort operations of sub-performing automated driving functions well before any accidents are caused.</p>