## 3 Localisation of a City Bus with GNSS
### Performance Evaluation of the GNSS-Based Localisation System
Based on these plots, it can be seen that there are some relatively long parts of the trajectories, especially in Figure 4B,C (marked with numbers (1) and (2)) where only the standard mode is available. These parts are the narrow streets with dense canopies of trees and high-rise buildings placed along the road, as shown in Figure 5, that disturb the GNSS signal and increase the localisation error. Unfortunately, the structure of the environment significantly influences the quality of GNSS localisation, and such cases are inevitable [19; 22]. Figure 6 presents the error between _moving_base_ and _rover_, calculated in an analogous manner as in Table 2, but this time only for the chosen part of the Garbary PKM-Os. Debina trajectory. The statistical means of this error is presented in Table 3. Based on these results, it can be stated that the accuracy of GNSS-based localisation systems indeed deteriorates in these areas, as the values of an error calculated for this case are greater than for the whole sequence. Interestingly, the RMS and mean values increased by around 0.16  m and 0.2 m accordingly, but the standard deviation of the discussed error did not change significantly. This can suggest that positions in the non-RTK mode have a constant offset which can be caused, for example, by the multipathing of GNSS signals. \begin{table}
\begin{tabular}{c c c c c} \hline
**Sequence (Bus Stops)** & **ATE\({}_{\text{RMS}}\)** & **ATE\({}_{\text{max}}\)** & **ATE\({}_{\text{mean}}\)** & **ATE\({}_{\text{std}}\)** \\ \hline Dworzec Zachodni–Kacza & 0.276 m & 3.113 m & 0.175 m & 0.213 m \\ Garbary PKM–Strzeszyn & 0.602 m & 4.377 m & 0.428 m & 0.423 m \\ Garbary PKM–Os. Debina & 0.619 m & 4.340 m & 0.440 m & 0.436 m \\ \hline \end{tabular}
\end{table} 
Table 2: Absolute Trajectory Error calculated between the poses provided by the _moving_base_ and _rover_ receivers. For the error calculation, the _rover_ poses were transformed to the _moving_base_ frame. Columns contain the sequence name and statistics (RMS, max, mean and standard deviation) of the error, respectively. Figure 4: The recorded routes of the city bus plotted on the _OpenStreetMap_. RTK FIXED poses are marked with the blue line, less accurate RTK FLOAT poses are marked with a red line, and the least accurate standard GNSS poses are marked with the yellow line. Plot (**A**) shows the map for Dworzec Zachodni–Kacza, (**B**) for Garbary PKM–Strzeszyn, and (**C**) for Garbary PKM–Os. Debina route. However, from our point of view, the most important part of the trajectory is the area near the charging station, where the system is supposed to operate. Our ADAS can guide the bus driver to any charging station of the chosen type (in the demo application, this is the EC Ride & Charge type), as long as the geographical location of the charger in terms of the WGS84 coordinates is known. Moreover, to plan safe manoeuvres, we need to know the local map of the charger's surroundings, which allows the ADAS to avoid static obstacles (pavement, lawns, etc.). As the considered ADAS hardware setup does not contain any external sensors to perceive the environment, we adopted the freely available OpenStreetMap data for that purpose. For the experimental evaluation presented in this article, the ADAS was configured to work with an electric charger located at the bus depot neighbouring the Garbary PKM station. Hence, we paid particular attention to the surroundings of this location when assessing the GNSS localisation accuracy. Figure 7A shows the locations of all the charging stations at this depot and the one to which the bus drivers were guided by our system (marked with a green colour). Figure 7B presents the GNSS localisation mode for the analysed area, and as can be seen, for the whole approach path, the RTK FIXED mode of the GNSS receiver is available.