论文标题
通过贝叶斯模型进行统计软件测试的贝叶斯模型的集体风险最小化
Collective Risk Minimization via a Bayesian Model for Statistical Software Testing
论文作者
论文摘要
在过去的四年中，在加利福尼亚街道上部署的不同自动驾驶汽车平台的数量增加了6倍，而据报道的事故增加了12倍。这可能会成为一种趋势，没有迹象，因为硬件传感器和机器学习软件中的创新持续发展所激发。同时，如果我们期望公众和监管机构信任自动驾驶汽车平台，我们需要找到更好的方法来解决添加技术复杂性的问题而不增加事故风险。我们从可靠性工程的角度研究了这个问题，在这种可靠性工程中，给定的事故风险的严重性和可能发生。及时有关事故的信息对于工程师来说，要预测和重复使用以前的失败，以近似新城市发生事故的风险。但是，在自动驾驶汽车的背景下，由于操作方案（新城市驱动轨迹）的数据性质稀少，因此这是具有挑战性的。我们的方法是通过监视多车辆操作来减少状态空间来减轻数据稀疏性。然后，我们通过确定每个等价类别的测试的正确分配来最大程度地减少事故的风险。我们的贡献包括（1）一组策略，以监视多个自动驾驶汽车的操作数据，（2）估算事故风险的变化的贝叶斯模型，以及（3）反馈控制环，该反馈通过重新关注测试工作来最大程度地降低这些风险。从某种意义上说，我们能够衡量和控制操作方案变化多样性的风险，这是有希望的。我们使用来自两个具有不同流量模式的真实城市的数据评估了我们的模型，并将数据可用于社区。
In the last four years, the number of distinct autonomous vehicles platforms deployed in the streets of California increased 6-fold, while the reported accidents increased 12-fold. This can become a trend with no signs of subsiding as it is fueled by a constant stream of innovations in hardware sensors and machine learning software. Meanwhile, if we expect the public and regulators to trust the autonomous vehicle platforms, we need to find better ways to solve the problem of adding technological complexity without increasing the risk of accidents. We studied this problem from the perspective of reliability engineering in which a given risk of an accident has severity and probability of occurring. Timely information on accidents is important for engineers to anticipate and reuse previous failures to approximate the risk of accidents in a new city. However, this is challenging in the context of autonomous vehicles because of the sparse nature of data on the operational scenarios (driving trajectories in a new city). Our approach was to mitigate data sparsity by reducing the state space through monitoring of multiple-vehicles operations. We then minimized the risk of accidents by determining proper allocation of tests for each equivalence class. Our contributions comprise (1) a set of strategies to monitor the operational data of multiple autonomous vehicles, (2) a Bayesian model that estimates changes in the risk of accidents, and (3) a feedback control-loop that minimizes these risks by reallocating test effort. Our results are promising in the sense that we were able to measure and control risk for a diversity of changes in the operational scenarios. We evaluated our models with data from two real cities with distinct traffic patterns and made the data available for the community.
