Performance Evaluation of GNSS Position Augmentation Methods for Autonomous Vehicles in Urban Environments

Swaminathan, Harihara Bharathy; Sommer, Aron; Becker, Andreas; Atzmueller, Martin

doi:10.3390/s22218419

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…The possibility of using GNSS data to monitor and analyse the behaviour of urban bus drivers, including the detection of unsafe situations, such as speeding or braking sharply, has been demonstrated in [22]. Unfortunately, accurate and reliable localisation based on GNSS data in urban areas is pledged by a number of problems, as there are multiple signal reflections, and the number of satellites visible at the same time is not always sufficient [23].…”

Section: Related Workmentioning

confidence: 99%

GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing

Esfandiyar,

Ćwian,

Nowicki

et al. 2023

Remote Sensing

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Modern public transportation in urban areas increasingly relies on high-capacity buses. At the same time, the share of electric vehicles is increasing to meet environmental standards. This introduces problems when charging these vehicles from chargers at bus stops, as untrained drivers often find it difficult to execute docking manoeuvres on the charger. A practical solution to this problem requires a suitable advanced driver-assistance system (ADAS), which is a system used to automatise and make safer some of the tasks involved in driving a vehicle. In the considered case, ADAS supports docking to the electric charging station, and thus, it must solve two issues: precise positioning of the bus relative to the charger and motion planning in a constrained space. This paper addresses these issues by employing GNSS-based positioning and optimisation-based planning, resulting in an affordable solution to the ADAS for the docking of electric buses while recharging. We focus on the practical side of the system, showing how the necessary features were attained at a limited hardware and installation cost, also demonstrating an extensive evaluation of the fielded ADAS for an operator of public transportation in the city of Poznań in Poland.

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Section: Related Workmentioning

confidence: 99%

GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing

Esfandiyar,

Ćwian,

Nowicki

et al. 2023

Remote Sensing

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“…Advanced techniques in satellite positioning, especially in the transport sector, can address various challenges, particularly in ensuring accurate positioning, resource allocation, and improving safety in applications that rely on Global Navigation Satellite Systems (GNSS) signals and services, including aviation, maritime navigation, and land transportation systems [12].…”

Section: Introductionmentioning

confidence: 99%

Enhancing accuracy of information processing in onboard subsystems of UAVs

Zhukov,

Dolintse

2023

TAPR

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The object of research is the onboard subsystems of Unmanned Aerial Vehicles (UAVs). The research is aimed at analyzing UAVs, specifically the integration and enhancement of satellite-based positioning systems, including Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). The problem concerns traditional satellite-based positioning services, especially those relying solely on medium earth orbit (MEO) satellites, which are insufficient for specific requirements. The study aims to address the limitations of these systems on onboard subsystems of UAVs, especially in challenging environments laden with jammers and interference, and to provide a more accurate, robust, and continuous positioning solution. The research proposes a «multilayer system of systems» approach that integrates signals from various sources, including low Earth orbit (LEO) satellites, ground-based positioning, navigation, and timing (PNT) systems, and user-centric sensors. The combined approach, termed LeGNSS/INS, leverages the strengths of each component, providing redundancy and enhanced accuracy. The system's performance was evaluated using pseudo-real output data, demonstrating its ability to generate quasi-real dynamic trajectories for UAV flight. The error analysis showed that the proposed method consistently outperforms traditional GNSS systems, especially in challenging environments. The enhanced performance of the LeGNSS/INS system can be attributed to integrating multiple satellite systems with INS and applying optimal filtering techniques. The research also employed mathematical modeling to represent the dependencies and interactions when combining data from different sources, such as GPS, LEO, and INS. The Kalman filter is a mechanism to fuse data from multiple sources optimally. The insights from this study apply to various sectors, including aviation, maritime navigation, autonomous drones, and defense. The enhanced positioning accuracy can significantly improve safety, navigation precision, and operational efficiency. However, the study assumes idealized conditions for satellite signal reception, which might not always be accurate in real-world scenarios. Challenges, such as the martial law conditions in Ukraine affecting data collection and potential satellite signal restrictions, were also highlighted. Further research can delve into the impact of more complex environmental factors and the integration of additional satellite systems or sensors to enhance accuracy further.

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“…Research addressing the three aspects mentioned above will greatly promote the application of real-time high-precision GNSS positioning in the mass market and advance autonomous driving and urban digitization. This Special Issue, entitled “ Advances in GNSS Positioning and GNSS Remote Sensing” in Sensors ( , (accessed on 2 February 2024)), attracted 10 articles [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ] on new GNSS techniques for ionospheric studies, the impact of space weather on GNSS and methods for precise navigation.…”

mentioning

confidence: 99%

“…Swaminathan et al [ 16 ] thoroughly examined the techniques utilized to augment the GNSS position within the challenging urban scenario under three modes: differential GNSS, Real-time Kinematic (RTK), and Real-time eXtended (RTX). Using the Applanix POS-LV 220 navigation system and high-definition maps for validation, they studied how the receiver navigates through diverse scenarios, including uneven terrain, tall buildings, varying road widths, and tunnels.…”

mentioning

confidence: 99%