## 4 Conclusions  
The paper describes the automatic driving system of a mobile vehicle, which was equipped with two independent systems based on which the vehicle's position on the test track can be determined. The experiment assumes that GPS cannot be used. The first of the implemented systems calculated the robot positions from the robot wheels. This is the main system of the robot positioning in space, but its main disadvantage is the fact that the larger the distance travelled by the robot, the more positioning error increases. Therefore, the authors decided to develop a system that provides corrections from an independent system that gives an absolute measurement to known characteristic points with a well-known location on the track. The measurement to the markers was carried out using a vision system and if the marker or markers appeared in the field of view of the camera, the system began to use the detection algorithm and determines the correction needed. Additionally, the vehicle algorithm informed about this fact by lighting the blue lamp on board. The places where the system made corrections are shown in the screenshots and marked with arrows. During real track tests, the vehicle performed 15 drives. The robot was placed on the starting line and then we measured the positioning accuracy. It should be noted that the vehicle was not able to complete the full test drive, when the vision system was turned off, despite being equipped with a simple wheel slip detection system. This result was expected, due to the very sandy ground. A more sophisticated system would need to be used in the future. When the vision system was turned on, an approach to the first checkpoint was obtained with an accuracy of 2 cm, the worst result was obtained at the last checkpoint and it was 20 cm. This was because the checkpoint was laying at the top of a very steep hill. In this case, it may help, the algorithm described in [19]. It can be concluded that the laboratory tests overlapped with the real track tests. The addition of the vision system made the autonomous task possible to complete across the entire test track, with the required accuracy of 50 cm. Especially, when the obtained results were compared with GNSS with RTK results \(\pm\)15 cm presented in [23].  
During further research, it is worth considering replacing known graphical markers such as ARTags with arbitrary feature points from the vehicle environment. This will allow  
Figure 15: A mobile vehicle with a vision system on the test track. The red arrows point places where the vision system corrected the vehicle positions. The brown line is the path of the vehicle.  
authors to determine the position with higher accuracy. Currently, due to the increasing computational efficiency of microprocessor chips, the authors plan to implement vehicle tracking using robust SLAM and sparse point cloud on the vehicle board.  
**Author Contributions:** Supervising A.N.; conceptualization J.Z. and D.P.; methodology, J.Z.; software, J.Z. and D.P.; validation, J.N., J.Z. and D.P.; formal analysis, J.Z., J.N.; investigation, J.Z.; data curation, D.P.; writing--original draft preparation, J.Z.; visualization, D.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Lodz University of Technology, 116 Zeromskiego Street, Lodz 90-924, Poland and Kielce University of Technology, 7 1000-lecia P.P. Avenue, Kielce 25-314, Poland.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Authors would like to thank the Kielce University of Technology IMPULS Team, who won European Rover Challenge 2018, 2019 in Poland and University Rover Challenge 2019 in Utah USA.  
**Conflicts of Interest:** The authors declare no conflict of interest.