NRI: Receding Horizon Integrity-A New Navigation Safety Methodology for Co-Robotic Passenger Vehicles

这项研究的目的是确保自动驾驶汽车使用的车辆位置、航向和速度估计的完整性，以此作为生命关键决策的基础，例如启动和执行危险避免动作。完整性是对传感器信息的信任程度，已成功地在商用飞机上实施，以确保着陆等动作的安全。该项目解决了将完整性从航空应用程序转化为自动驾驶汽车的几个障碍，包括集成地面车辆使用的不同类型的传感器；满足常规自动驾驶的严格要求；说明道路上其他车辆的数量、距离和高相对速度；以及及时评估多个、不同和相互排斥的行动方案。项目子任务包括表征典型传感器的完整性，为不确定性传播构建适当的模型，以及对由此产生的完整性框架进行实验验证。该项目将推进更大规模的研究工作，以实现自动驾驶汽车在缓解拥堵、减少排放和拯救生命方面的潜力。这项工作包括在自动驾驶汽车自动导航方面的公众推广工作，这将建立在与芝加哥科学和工业博物馆的持续关系的基础上，包括在国家机器人周期间进行动手演示，以说明如何在与传感器读数、车辆动力学和驾驶环境相关的不确定性的情况下确保安全性。具体地说，这项研究将提供新的实验和分析方法来量化和证明自动驾驶汽车的安全性。这项工作的结果将创建一个高水平的、独立于传感器的可量化指标，可用于比较、评估和认证自动驾驶汽车制造商的安全性。将在几个以前未探索的领域推进知识，包括首次演示：1)非GPS传感器的高完整性传感器测量误差和故障模型；2)量化激光雷达、雷达和基于相机的定位所需的特征提取和数据关联算法的安全风险的分析方法；3)多传感器位姿估计器和完整性监测器，旨在评估未检测到的传感器故障对安全风险的影响；以及4)经过严格推导和实验验证的动态环境中的完整性风险预测方法。

The objective of this research is to ensure the integrity of vehicle position, heading, and velocity estimates that are used by self-driving cars as the basis for life-critical decisions such as the initiation and execution of hazard-avoidance maneuvers. Integrity, which is a measure of trust in a sensor's information, has been successfully implemented in commercial aircraft to guarantee the safety of maneuvers such as landing. This project addresses several obstacles in translating integrity from aviation applications to self-driving cars, including integrating the disparate sensor types used by ground vehicles; meeting the stringent demands of routine autonomous driving; accounting for the number, proximity, and high relative velocity of other vehicles on the road; and evaluating multiple, distinct, and mutually exclusive courses of action in a timely manner. Project subtasks include characterization of integrity for representative sensors, construction of appropriate models for uncertainty propagation, and experimental validation of the resulting integrity framework. The project will advance the larger research effort to realize the potential of self-driving cars for relieving congestion, reducing emissions, and saving lives. The work includes public outreach efforts on autonomous navigation for self-driving cars, which will build upon an ongoing relationship with Chicago's Museum of Science and Industry, including a hands-on demonstration during National Robotics Week to illustrate how safety can be ensured despite uncertainties related to sensor readings, vehicle dynamics, and the driving environment.Specifically, this research will provide new experimental and analytical methods to quantify and prove self-driving car safety. The results of this work will create a high-level, sensor-independent, quantifiable metric that can be used to compare, evaluate, and certify safety across self-driving car manufacturers. Knowledge will be advanced in several previously-unexplored areas, including first-ever demonstrations of: 1) high-integrity sensor measurement error and fault models for non-GPS sensors, 2) analytical methods to quantify the safety risk of feature extraction and data association algorithms required in lidar, radar, and camera-based localization, 3) multi-sensor pose estimators and integrity monitors designed to evaluate the impact of undetected sensor faults on safety risk, and 4) rigorously derived and experimentally validated integrity risk prediction methods in dynamic environments.