Verifiable Obstacle Detection
Verifiable Obstacle Detection
复制标题
DOI：
10.1109/issre55969.2022.00017
复制
发表时间：
2022-08
期刊：
影响因子：
--
通讯作者：
Ayoosh Bansal;Hunmin Kim;Simon Yu;Bo-Yi Li;N. Hovakimyan;M. Caccamo;L. Sha
中科院分区：
文献类型：
--
作者：
Ayoosh Bansal;Hunmin Kim;Simon Yu;Bo-Yi Li;N. Hovakimyan;M. Caccamo;L. Sha
对障碍物的感知仍然是自动驾驶汽车的一个关键安全问题。现实世界的碰撞表明，导致致命碰撞的自主故障源于障碍物存在检测。开源自动驾驶实现展示了具有复杂的相互依赖的深度神经网络的感知管道。这些网络无法完全验证，因此不适合安全关键任务。在这项工作中，我们对现有的基于激光雷达的经典障碍物检测算法进行了安全验证。我们对该障碍物检测算法的功能建立了严格的限制。给定安全标准，这样的界限允许确定能够可靠地满足标准的 LiDAR 传感器属性。对于基于神经网络的感知系统来说，这种分析尚无法实现。我们根据真实世界的传感器数据提供了对障碍物检测系统的严格分析和实证结果。
Perception of obstacles remains a critical safety concern for autonomous vehicles. Real-world collisions have shown that the autonomy faults leading to fatal collisions originate from obstacle existence detection. Open source autonomous driving implementations show a perception pipeline with complex interdependent Deep Neural Networks. These networks are not fully verifiable, making them unsuitable for safety-critical tasks. In this work, we present a safety verification of an existing LiDAR based classical obstacle detection algorithm. We establish strict bounds on the capabilities of this obstacle detection algorithm. Given safety standards, such bounds allow for determining LiDAR sensor properties that would reliably satisfy the standards. Such analysis has as yet been unattainable for neural network based perception systems. We provide a rigorous analysis of the obstacle detection system with empirical results based on real-world sensor data.