Latest revision as of 22:37, 1 February 2021

Abstract

High-precision and lane selective position estimation is of fundamental importance for prospective advanced driver assistance systems (ADAS) and automated driving functions, as well as for traffic information and management processes in intelligent transportation systems (ITS). User and vehicle positioning is usually based on Global Navigation Satellite System (GNSS), which, as stand-alone positioning, does not meet the necessary requirements in terms of accuracy. Furthermore, the rise of connected driving offers various possibilities to enhance GNSS positioning by applying cooperative positioning (CP) methods. Utilizing only low-cost sensors, especially in urban environments, GNSS CP faces several demanding challenges. Therefore, this contribution presents an empirical study on how Vehicle-to-Everything (V2X) technologies can aid GNSS position estimation in urban environments, with the focus being solely on positioning performance instead of multi-sensor data fusion. The performance of CP utilizing common positioning approaches as well as CP integration in state-of-the-art Vehicular Ad-hoc Networks (VANET) is displayed and discussed. Additionally, a measurement campaign, providing a representational foundation for validating multiple CP methods using only consumer level and low-cost GNSS receivers, as well as commercially available IEEE 802.11p V2X communication modules in a typical urban environment is presented. Evaluating the algorithm&rsquo

s performance, it is shown that CP approaches are less accurate compared to single positioning in the given environment. In order to investigate error influences, a skyview modelling seeking to identify non-line-of-sight (NLoS) effects using a 3D building model was performed. We found the position estimates to be less accurate in areas which are affected by NLoS effects such as multipath reception. Due to covariance propagation, the accuracy of CP approaches is decreased, calling for strategies for multipath detection and mitigation. In summary, this contribution will provide insights on integration, implementation strategies and accuracy performances, as well as drawbacks for local area, low-cost GNSS CP in urban environments.

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The different versions of the original document can be found in:

• http://dx.doi.org/10.3390/s19235201 under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.3390/s19235201 under the license http://creativecommons.org/licenses/by/3.0/

• http://europepmc.org/articles/PMC6929133 under the license https://creativecommons.org/licenses/by/4.0

• https://www.mdpi.com/1424-8220/19/23/5201/pdf

• https://dblp.uni-trier.de/db/journals/sensors/sensors19.html#SchwarzbachMTM19,

https://www.mdpi.com/1424-8220/19/23/5201,

https://www.mdpi.com/1424-8220/19/23/5201/pdf,

https://doi.org/10.3390/s19235201,

https://academic.microsoft.com/#/detail/2989653715 under the license cc-by

• https://www.mdpi.com/1424-8220/19/23/5201/pdf,

http://dx.doi.org/10.3390/s19235201

• [ ] under the license https://creativecommons.org/licenses/by/4.0/