由于全景分割为输入中的每个像素提供了一个预测，因此，非标准和看不见的对象系统地导致了错误的输出。但是，在关键的环境中，针对分发样本的鲁棒性和角案件对于避免危险行为至关重要，例如忽略动物或道路上的货物丢失。由于驾驶数据集不能包含足够的数据点来正确采样基础分布的长尾巴，因此方法必须处理未知和看不见的方案才能安全部署。以前的方法是通过重新识别已经看到未标记的对象来针对此问题的一部分。在这项工作中，我们扩大了提出整体分割的范围：一项任务，以识别和将看不见的对象分为实例，而无需从未知数中学习，同时执行已知类别的全面分割。我们用U3HS解决了这个新问题，U3HS首先将未知数视为高度不确定的区域，然后将相应的实例感知嵌入到各个对象中。通过这样做，这是第一次使用未知对象进行综合分割，我们的U3HS未接受未知数据的训练，因此使对象类型的设置不受限制，并允许对整体场景理解。在两个公共数据集上进行了广泛的实验和比较，即CityScapes和作为转移的丢失和发现，证明了U3HS在挑战性的整体分段任务中的有效性，并具有竞争性的封闭式全盘分段性能。

The promise of increased road safety is a key motivator for the development of automated vehicles (AV). Yet, demonstrating that an AV is as safe as, or even safer than, a human-driven vehicle has proven to be challenging. Should an AV be examined purely virtually, allowing large numbers of fully controllable tests? Or should it be tested under real environmental conditions on a proving ground? Since different test setups have different strengths and weaknesses, it is still an open question how virtual and real tests should be combined. On the way to answer this question, this paper proposes transfer importance sampling (TIS), a risk estimation method linking different test setups. Fusing the concepts of transfer learning and importance sampling, TIS uses a scalable, cost-effective test setup to comprehensively explore an AV's behavior. The insights gained then allow parameterizing tests in a more trustworthy test setup accurately reflecting risks. We show that when using a trustworthy test setup alone is prohibitively expensive, linking it to a scalable test setup can increase efficiency $\unicode{x2013}$ without sacrificing the result's validity. Thus, the test setups' individual deficiencies are compensated for by their systematic linkage.