Paracosm：自动驾驶系统测试语言和工具
Paracosm: A Language and Tool for Testing Autonomous Driving Systems
摘要：
在复杂的现实世界场景中对自动驾驶汽车进行系统测试是一个困难且昂贵的问题。我们提出了Paracosm，一种用于编写自动驾驶系统测试场景的反应式语言。Paracosm允许用户以编程方式描述具有特定视觉特征的复杂驾驶情况，例如，城市环境中的道路布局，以及汽车和行人的反应性时间行为。Paracosm程序在游戏引擎之上执行，提供逼真的物理模拟和视觉渲染。该基础设施允许对状态空间进行系统的探索，包括视觉特征（照明，阴影，雾）和与环境的反应性交互（行人，其他交通）。我们定义了一个概念的Paracosm配置的组合测试和低分散序列的基础上的测试覆盖率。Paracosm附带了一个自动测试用例生成器，它对离散参数使用随机采样，对连续参数使用确定性准蒙特卡罗生成。通过实证评估，我们展示了Paracosm在一套使用研究和教育中开发的深度神经网络实现的自动驾驶系统上的建模和测试能力。我们展示了Paracosm如何暴露不正确的行为或降低性能。
英文摘要：
Systematic testing of autonomous vehicles operating in complex real-world scenarios is a difficult and expensive problem. We present Paracosm, a reactive language for writing test scenarios for autonomous driving systems. Paracosm allows users to programmatically describe complex driving situations with specific visual features, e.g., road layout in an urban environment, as well as reactive temporal behaviors of cars and pedestrians. Paracosm programs are executed on top of a game engine that provides realistic physics simulation and visual rendering. The infrastructure allows systematic exploration of the state space, both for visual features (lighting, shadows, fog) and for reactive interactions with the environment (pedestrians, other traffic). We define a notion of test coverage for Paracosm configurations based on combinatorial testing and low dispersion sequences. Paracosm comes with an automatic test case generator that uses random sampling for discrete parameters and deterministic quasi-Monte Carlo generation for continuous parameters. Through an empirical evaluation, we demonstrate the modeling and testing capabilities of Paracosm on a suite of autonomous driving systems implemented using deep neural networks developed in research and education. We show how Paracosm can expose incorrect behaviors or degraded performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
