# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 6 Conclusions
These results are more accurate and reliable compared to [4], where a RMSE 2D positioning distance error of around 30 cm was obtained in the best case scenario, and [5], where a RMSE 3D positioning distance error of 57 cm was achieved in the best case scenario. However, it should be pointed out that the used setup and the testing application are different than the ones of those publications. Meaning that, for having a fair comparison and, in consequence, extracting more conclusion about the different results the same setup and testing application should be used. Moreover, it can be said that these results overcome in a significant way the ones obtained directly from the RC car on-board data in which the maximum positioning distance error is higher than 4 m. It should be pointed out that, for enhancing the positioning performance, it is really important to avoid using inaccurate UWB ranging estimates in the correction phase of the EKF. In this work, post-processing of these data has been necessary for eliminating the NLOS UWB ranging estimates. By post-processing these data, the RMSE of the positioning distance error was reduced in almost 23 cm and the maximum positioning distance error in 127 cm. Therefore, it can be concluded that using UWB ranging estimates in those areas with no GNSS coverage enables to bound the positioning distance error of the position computed from the RC car on-board data. The use of UWB ranging estimates for computing the vehicle's position leads to a continuity in an accurate and reliable positioning over time. To summarise, we have shown the potentials of the UWB technology for enhancing the actual positioning technologies within the vehicle industry. The use of this technology in particular environments, such as tunnels or urban canyons, can overcome the limits that GNSS based positioningtechnologies are facing. The combination of the actual positioning technology together with UWB ranging estimates can be crucial for guaranteeing continuity in a precise positioning over time in the upcoming era of autonomous vehicles. Conceptualization, A.C. and L.Z.-C.; Formal analysis, A.C. and L.Z.-C.; Funding acquisition, A.C.; Investigation, A.C., L.Z.-C., J.S.M. and B.J.S. ; Methodology, L.Z.-C.; Project administration, A.C.; Software, L.Z.-C., J.S.M. and B.J.S. ; Supervision, A.C. and L.Z.-C.; Validation, J.S.M. and B.J.S. ; Writing--original draft, J.S.M. and A.C.; Writing--review & editing, A.C., L.Z.-C., B.J.S. and J.S.M. All authors have read and agreed to the published version of the manuscript. This research was funded by the European GNSS Agency under the PRoPART project, H2020 EU project, grant number 776307. This work has been possible thanks to the cooperation between CEIT and RISE, PRoPART partner and coordinator respectively. In particular, the authors would like to thank [PERSON] for his support. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} AKF & Adaptive Kalman Filter \\ CDF & Cumulative Distribution Function \\ EKF & Extended Kalman Filter \\ Fc & Central Frequency \\ GNSS & Global Navigation Satellite System \\ GPS & Global Positioning System \\ IMU & Inertial Measurement Unit \\ INS & Inertial Navigation System \\ IPS & Indoor Positioning System \\ KF & Kalman Filter \\ LOS & Line-of-Sight \\ NLOS & Non Line-of-Sight \\ PAKF & Predictive Adaptive Kalman Filter \\ PDR & Pedestrian Dead Reckoning \\ PRF & Pulse Repetition Frequency \\ RC car & Radio Control car \\ RISE & Research Institutes of Sweden \\ RKF & Robust Kalman Filter \\ RTK & Real Time Kinematic \\ RMSE & Root Mean Square Error \\ RSU & Road Side Unit \\ RTK-SN & Real-Time Kinematic Satellite Navigation \\ SHFAF & Sage-Husa fuzzy adaptive filter \\ UKF & Unscented Kalman Filter \\ UWB & Ultra-Wideband \\ V2V & Vehicle-to-Vehicle \\ V2X & Vehicle-to-Everything \\ \end{tabular}