Autonomous vehicles are complex systems that are challenging to test and
debug. A requirements-driven approach to the development process can decrease
the resources required to design and test these systems, while simultaneously
increasing the reliability. We present a testing framework that uses signal
temporal logic (STL), which is a precise and unambiguous requirements language.
Our framework evaluates test cases against the STL formulae and additionally
uses the requirements to automatically identify test cases that fail to satisfy
the requirements. One of the key features of our tool is the support for
machine learning (ML) components in the system design, such as deep neural
networks. The framework allows evaluation of the control algorithms, including
the ML components, and it also includes models of CCD camera, lidar, and radar
sensors, as well as the vehicle environment. We use multiple methods to
generate test cases, including covering arrays, which is an efficient method to
search discrete variable spaces. The resulting test cases can be used to debug
the controller design by identifying controller behaviors that do not satisfy
requirements. The test cases can also enhance the testing phase of development
by identifying critical corner cases that correspond to the limits of the
system's allowed behaviors. We present STL requirements for an autonomous
vehicle system, which capture both component-level and system-level behaviors.
Additionally, we present three driving scenarios and demonstrate how our
requirements-driven testing framework can be used to identify critical system
behaviors, which can be used to support the development process.

提出了一种使用信号时序逻辑（STL）的测试框架，以实现需求驱动的方法来测试自动驾驶汽车的复杂系统，该框架支持机器学习等深度神经网络组件，可以评估控制算法和包括车载环境的感应器模型，提供能够增强控制器设计调试和测试可靠性的测试用例。