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

NRI：后退地平线完整性——协作机器人乘用车的新导航安全方法

基本信息

批准号：
1637899
负责人：
Matthew Spenko
金额：
$ 89.99万
依托单位：
Illinois Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637899&HistoricalAwards=false
关键词：
NRI Receding Horizon Integrity New

项目摘要

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.

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，作为生命攸关决策的基础，例如启动和执行危险规避操作。完整性是对传感器信息的信任程度，已在商用飞机中成功实施，以保证着陆等操作的安全。该项目解决了将完整性从航空应用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；满足日常自动驾驶的严格要求；考虑道路上其他车辆的数量、接近度和高相对速度；及时评估多种、不同且相互排斥的行动方案。项目子任务包括表征传感器的完整性、构建适当的不确定性传播模型以及对所得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。这项工作包括有关自动驾驶汽车自主导航的公共宣传工作，该工作将建立在与芝加哥科学与工业博物馆的持续关系基础上，包括在国家机器人周期间进行实践演示，以说明如何在存在相关不确定性的情况下确保安全具体来说，这项研究将为量化和证明自动驾驶汽车的安全性提供新的实验和分析方法。这项工作的结果将创建一个高级的、独立于传感器的、可量化的指标，可用于比较、评估和认证自动驾驶汽车制造商的安全性。将在几个先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）量化特征提取和数据关联的安全风险的分析方法激光雷达、雷达和基于摄像头的定位所需的算法，3) 多传感器姿态估计器和完整性监视器，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) 严格推导和实验验证的完整性风险动态环境中的预测方法。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Local Nearest Neighbor Integrity Risk Evaluation for Robot Navigation

机器人导航的局部最近邻完整性风险评估

DOI：
发表时间：
2018-05
期刊：
IEEE International Conference on Robotics and Automation
影响因子：
0
作者：
Arana, Guillermo Duenas;Joerger, Mathieu;Spenko, Matthew
通讯作者：
Spenko, Matthew

Landmark Selection and Unmapped Obstacle Detection in Lidar-Based Navigation

基于激光雷达的导航中的地标选择和未映射的障碍物检测

DOI：
发表时间：
2017-09
期刊：
Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017
影响因子：
0
作者：
Joerger, Mathieu;Arana, Guillermo Duenas;Spenko, Matthew;Pervan, Boris
通讯作者：
Pervan, Boris

On Robot Localization Safety for Fixed-Lag Smoothing: Quantifying the Risk of Misassociation

固定滞后平滑的机器人定位安全：量化错误关联的风险

DOI：
10.1109/plans46316.2020.9110126
发表时间：
2020-04
期刊：
Location and Navigation Symposium (PLANS
影响因子：
0
作者：
Hafez, Osama Abdul;Arana, Guillermo Duenas;Chen, Yihe;Joerger, Mathieu;Spenko, Matthew
通讯作者：
Spenko, Matthew

Recursive Integrity Monitoring for Mobile Robot Localization Safety

移动机器人定位安全的递归完整性监控