國立虎尾科技大學 |

High-Integrity Urban Localization : = Bringing Safety in Aviation to Autonomous Driving.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	High-Integrity Urban Localization :/
Reminder of title:	Bringing Safety in Aviation to Autonomous Driving.
Author:	Gupta, Shubh.
Description:	1 online resource (146 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	False alarms. -
Online resource:	click for full text (PQDT)
ISBN:	9798381020601

High-Integrity Urban Localization : = Bringing Safety in Aviation to Autonomous Driving.
Gupta, Shubh.

High-Integrity Urban Localization :Bringing Safety in Aviation to Autonomous Driving. - 1 online resource (146 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

Includes bibliographical references

Localization is a crucial function for autonomous vehicles (AVs), as it influences critical tasks such as perception, planning, and control. However, most existing localization systems focus on accuracy and may fail unexpectedly in challenging conditions, posing safety risks. To address this issue, this thesis proposes novel algorithms that prioritize safety along with accuracy for localization. These algorithms not only robustly estimate the location, but also continuously assess its reliability.Drawing inspiration from the principles of integrity monitoring - a safety-centric framework used in aviation for reliable Global Navigation Satellite System (GNSS)-based localization - this thesis aims to adapt these principles for assessing location reliability in autonomous driving. However, directly applying this framework to autonomous driving, particularly in urban environments, presents significant challenges. Unlike the well-characterized GNSS errors observed in open-sky conditions of aviation, localization errors in urban settings are more unpredictable and severe. Moreover, the variety of sensors employed in AV localization, such as GNSS, cameras, and inertial sensors, require effectively handling the limitations and combined effects of each sensor on the localization process.In this thesis, we present novel algorithms that utilize different sensors to improve localization and enable integrity monitoring in urban environments. These algorithms are tailored to handle various challenges that affect the reliability of the localization process, such as large errors in multiple GNSS measurements, high-dimensional errors in visual measurements, and combined effects of errors from different sensors. For these challenges, we develop strategies to mitigate the impact of large errors on localization, estimate the error bounds of the localization solution, or detect when the localization error exceeds a threshold. We also describe how these algorithms can be implemented efficiently for fast computation in AVs, and show how they provide a better understanding of the localization reliability than existing methods.We test our algorithms on simulated and real-world data from urban environments. The results show that our algorithms can estimate the vehicle's position reliably and robustly, and also evaluate the integrity of the localization solution.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381020601Subjects--Topical Terms:

1470428
False alarms.
Index Terms--Genre/Form:

554714
Electronic books.

High-Integrity Urban Localization : = Bringing Safety in Aviation to Autonomous Driving.
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502 $a Thesis (Ph.D.)--Stanford University, 2023.
504 $a Includes bibliographical references
520 $a Localization is a crucial function for autonomous vehicles (AVs), as it influences critical tasks such as perception, planning, and control. However, most existing localization systems focus on accuracy and may fail unexpectedly in challenging conditions, posing safety risks. To address this issue, this thesis proposes novel algorithms that prioritize safety along with accuracy for localization. These algorithms not only robustly estimate the location, but also continuously assess its reliability.Drawing inspiration from the principles of integrity monitoring - a safety-centric framework used in aviation for reliable Global Navigation Satellite System (GNSS)-based localization - this thesis aims to adapt these principles for assessing location reliability in autonomous driving. However, directly applying this framework to autonomous driving, particularly in urban environments, presents significant challenges. Unlike the well-characterized GNSS errors observed in open-sky conditions of aviation, localization errors in urban settings are more unpredictable and severe. Moreover, the variety of sensors employed in AV localization, such as GNSS, cameras, and inertial sensors, require effectively handling the limitations and combined effects of each sensor on the localization process.In this thesis, we present novel algorithms that utilize different sensors to improve localization and enable integrity monitoring in urban environments. These algorithms are tailored to handle various challenges that affect the reliability of the localization process, such as large errors in multiple GNSS measurements, high-dimensional errors in visual measurements, and combined effects of errors from different sensors. For these challenges, we develop strategies to mitigate the impact of large errors on localization, estimate the error bounds of the localization solution, or detect when the localization error exceeds a threshold. We also describe how these algorithms can be implemented efficiently for fast computation in AVs, and show how they provide a better understanding of the localization reliability than existing methods.We test our algorithms on simulated and real-world data from urban environments. The results show that our algorithms can estimate the vehicle's position reliably and robustly, and also evaluate the integrity of the localization solution.
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