论文标题
SceneChecker：使用对称抽象来提高场景验证
SceneChecker: Boosting Scenario Verification using Symmetry Abstractions
论文作者
论文摘要
我们介绍了Cenechecker，该工具用于验证涉及大型杂乱工作空间中复杂计划的车辆的场景。 SceneChecker将场景验证问题转换为标准混合系统验证问题，并通过利用计划中的结构性和车辆动力学来有效地解决它。 SceneChecker使用对称抽象，一种新颖的改进算法，并且重要的是，构建的是为了提高任何现有的可及性分析工具作为插件子例程的性能。我们在涉及具有非线性动力学和神经网络控制器的地面和航空车辆的几种场景上评估了场景，采用了不同类型的对称性，使用不同的可及性子例程，以及在复杂工作区中具有数百个方向的计划。与两种领先的工具（Dryvr和Flow*）相比，SceneChecker在验证时间内显示20倍加速度，即使将这些非常非常的工具作为可及性子例程。
We presentSceneChecker, a tool for verifying scenarios involving vehicles executing complex plans in large cluttered workspaces. SceneChecker converts the scenario verification problem to a standard hybrid system verification problem, and solves it effectively by exploiting structural properties in the plan and the vehicle dynamics. SceneChecker uses symmetry abstractions, a novel refinement algorithm, and importantly, is built to boost the performance of any existing reachability analysis tool as a plug-in subroutine. We evaluated SceneChecker on several scenarios involving ground and aerial vehicles with nonlinear dynamics and neural network controllers, employing different kinds of symmetries, using different reachability subroutines, and following plans with hundreds of way-points in complex workspaces. Compared to two leading tools, DryVR and Flow*, SceneChecker shows 20x speedup in verification time, even while using those very tools as reachability subroutines.
