论文标题
转移重要性抽样 - 在多个测试设置中测试自动化车辆如何有助于偏置折衷方案
Transfer Importance Sampling -- How Testing Automated Vehicles in Multiple Test Setups Helps With the Bias-Variance Tradeoff
论文作者
论文摘要
增加道路安全的承诺是开发自动车辆（AV）的关键动机。然而，证明AV比人类驱动的车辆更安全，甚至更安全，事实证明是具有挑战性的。是否应该纯粹地检查AV，以允许大量完全可控制的测试？还是应该在实际环境条件下在试验场上进行测试？由于不同的测试设置具有不同的优势和劣势，因此如何将虚拟和真实测试结合在一起仍然是一个悬而未决的问题。在回答这个问题的路上，本文提出了转移重要性抽样（TIS），这是一种连接不同测试设置的风险估计方法。融合了转移学习和重要性采样的概念，TIS使用可扩展的，具有成本效益的测试设置来全面探索AV的行为。然后获得的见解允许在更值得信赖的测试设置中进行参数化测试，以准确反映风险。我们表明，仅使用值得信赖的测试设置时，它非常昂贵，将其链接到可扩展的测试设置可以提高效率$ \ unicode {x2013} $，而不会牺牲结果的有效性。因此，测试设置的单个缺陷被其系统的链接所弥补。
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.
