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Assured Precise Point Positioning Techniques Driving the Future

Authors: D. Calle; E. Carbonell; A. Chamorro; A. González; C. Mezzera; P. Navarro; D. Rodríguez; +2 Authors

Assured Precise Point Positioning Techniques Driving the Future

Abstract

Recent evolutions in the field of autonomous vehicles have made of GNSS positioning technology a cornerstone to ensure high-levels of navigation autonomy, since it has the capability of offering safe, secure, absolute and accurate positioning. This trend is found in different sectors and applications, such as drones, ground robots, autonomous vessels, etc. But undoubtedly one of the most active sectors in this evolution is the automotive one. Car manufacturers are in a continuous race to offer novel services to their customers, aiming to get new market niches. Currently, this race is focused on two main targets. First, “greenify” their fleets as they move from fossil fuels to green fuels. Then, the development of the autonomous driving services (ADAS) to achieve maximum possible autonomy. There are five main levels of autonomous driving defined. Level-1 is the most basic one, which provides driver assistance capabilities such as the adaptive cruise control and lane keep assist. Levels 2 and 3 introduce partial and conditional automation. Finally, Levels 4 and 5 represent high-and full automation. The target of the vehicle manufacturers is to reach the medium and upper levels of this rank in the upcoming years. This imposes new requirements in terms of higher levels of safety and accuracy to assure the reliability of the overall service. In this context, GNSS, and more concretely Assured Precise Point Positioning (APPP) technique, plays a key role in the development of these systems. APPP is a perfect complement to other vehicle sensors to populate and construct the ADAS safety case. It merges independent sources of reliable information needed to achieve highly-demanding safety goals. In the last years, GMV’s research and development activities have led to an end-to-end product compliant with the required safety and performance requirements of these high-demanding ADAS systems. The magicGNSS APPP product is an evolution of the magicGNSS Corrections service and magicPPP user algorithm. It complies with the safety standards applicable for automotive, ISO26262 and ISO/PAS 21448 (SOTIF), and the required integrity levels for the target application, up to 10-7/h. magicGNSS APPP solution relies on the use of an automotive grade equipment providing the needed safety mechanisms; it comprises a GNSS antenna, a GNSS receiver and an IMU compliant with ASIL-B, which are integrated with an advanced PPP algorithm. The later incorporates techniques and safety monitors that allow to obtain the maximum performance in terms of convergence, accuracy, velocity, heading and safety protection levels. During 2020-2021, the integration of the magicGNSS APPP in vehicle OEMs’ system is becoming a reality. This integration stage requires a strong effort on verification and validation to ensure the proper functioning of all the elements which are part of the chain. The goal is to achieve the system performance targets with the required level of safety. This paper presents GMV’s magicGNSS APPP solution for autonomous applications in its operational shape. The paper will describe the system architecture of the solution, introduce algorithms employed in the solution and techniques which support it. Additionally, an extensive experimentation campaign based on thousands of kilometres of real driving tests have been performed and carefully evaluated in terms of accuracy and safety. The results and statistics accumulated throughout this experimentation campaign will be presented and analysed.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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