In view of system reliability, extraction of knowledge from models of artificial intelligence may be more important than their forecasting ability. The elaboration of rules found by explainable artificial intelligence gives here insight into the problem of packet falsification in vehicle platooning. Detection and countermeasure are designed on the basis of feature and value ranking as well as rule confidence and they are validated under a large range of working conditions. The certification of safe operating conditions is found by achieving (statistically) zero false negatives, namely, the operating conditions predicted as ‘safe’ never lead to collision despite the cyber attack.

从系统可靠性的角度来看，从人工智能模型中提取知识可能比预测能力更重要。通过可解释的人工智能发现的规则的详细说明，在这里可以深入了解车辆排队中的数据包伪造问题。检测和对策是基于特征和值排序以及规则置信度设计的，它们在大范围的工作条件下得到验证。安全操作条件的认证是通过实现（统计上）零误报来发现的，即，尽管存在网络攻击，预测为“安全”的操作条件永远不会导致碰撞。