Unlike the version using a vision system [5], the implemented variant can also operate at night and in very poor weather conditions (snow, heavy rain). Compared to the version described in [3], the approach to the trajectory planning problem was also changed, mainly because we decided to use OpenStreetMaps data to automatically generate maps of obstacles limiting vehicle manoeuvring in the vicinity of the charging station. All these changes resulted in a low-cost system that can be retrofitted to almost any existing electric bus and makes it possible to guide the inexperienced driver to the charging station reliably. One should also note that many bus operators do not permit the installation of any additional elements on the existing charging stations, and they even do not allow altering the appearance of these structures. Hence, the minimalistic approach we present in our ADAS, which integrates only a few off-the-shelf components, and leverages as much as possible the available information sources (GNSS, publicly distributed corrections, OpenStreetMaps) is of great practical importance. The contributions of this paper are threefold. * Our new approach to providing the ADAS system with precise vehicle pose estimates using only GNSS and internal bus sensors contributes to the area of GNSS for urban transport applications. * A novel manoeuvre-planning algorithm that leverages affordable information sources (RTK GNSS, OpenStreetMap) and applies an optimisation technique to generate feasible trajectories under kinematic and geometric constraints in real time contributes to the area of motion planning for ADAS. * As far as we know, this paper is the first to report the results of long-term extensive tests with a GNSS-based ADAS for buses in a real urban environment under regular daily traffic with passengers. Hence, it contributes to a better understanding of GNSS localisation as a means of supporting precise manoeuvres of city buses. As the results are obtained on a regular bus route under day-by-day operation conditions, we were not able to deploy any other measurement system providing ground truth. Therefore, a direct comparison to other existing solutions is impossible, but we provide a unique insight into the production challenges when considering such a system in the final application. The remainder of this paper is structured as follows: Section 2 provides an overview of the most related publications concerning ADAS and the use of GNSS in public transportation. Section 3 describes our GNSS-based precise localisation solution and reports the general performance of this localisation modality while tracking the bus on its entire route. Then, Section 4 describes the approach to path planning and path-tracking control for the docking manoeuvres, while Section 5 gives an overview of the man-machine interface used by the driver. The extended experiments are described in Section 6, providing quantitative data on the accuracy of docking and path following, and a qualitative description of the driver's behaviour, including the observed issues and failure cases. Finally, the paper is concluded in Section 7 with an outlook of the possible future developments.