一种高效的可达性框架，用于未知环境中可证明安全的自主导航
An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments
AI总结：
提出一种基于Hamilton-Jacobi可达性的高效实时安全分析框架，在未知环境中为自主导航提供可证明的安全保证，且不依赖特定规划器，并已在仿真和硬件上验证。
摘要：
现实世界中的自动驾驶车辆通常在先验未知的环境中运行。由于这些系统大多属于安全关键系统，因此确保它们在面对环境不确定性（如未见障碍物）时安全运行至关重要。当前的安全分析工具使自主系统能够在预先完全了解环境状态的情况下推理安全性。然而，这些工具在环境被实时感知的场景（如导航任务期间）中扩展性不佳。在这项工作中，我们提出了一种基于Hamilton-Jacobi可达性的新颖实时安全分析方法，该方法在环境不确定性下提供了强大的安全保证。我们的安全方法不依赖于特定规划器，并为多种映射传感器提供保证。我们在仿真和硬件中演示了我们的方法，为一个最先进的基于视觉、基于学习的规划器提供安全保证。
英文摘要：
Real-world autonomous vehicles often operate in a priori unknown environments. Since most of these systems are safety-critical, it is important to ensure they operate safely in the face of environment uncertainty, such as unseen obstacles. Current safety analysis tools enable autonomous systems to reason about safety given full information about the state of the environment a priori. However, these tools do not scale well to scenarios where the environment is being sensed in real time, such as during navigation tasks. In this work, we propose a novel, real-time safety analysis method based on Hamilton-Jacobi reachability that provides strong safety guarantees despite environment uncertainty. Our safety method is planner-agnostic and provides guarantees for a variety of mapping sensors. We demonstrate our approach in simulation and in hardware to provide safety guarantees around a state-of-the-art vision-based, learning-based planner.
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。
