## 3 Results and Discussion
In a box plot, numerical data are divided into quartiles, and a box is drawn between the first and third quartiles, with an additional line drawn along the second quartile to mark the median. The minimums and maximums outside the first and third quartiles are depicted with lines, which are often called _whiskers_. The mean value is marked by \"x\" symbol. Figure 5: Driving time for 3 camera configurations and driving with direct observation of the surrounding—trial 1. Figure 6: Reaching the obstacle time for 3 camera configurations and driving with direct observation of the surrounding—trial 1—task 1. Figure 7: Cornering time for 3 camera configurations and driving with direct observation of the surrounding—trial 1—task 2. By analyzing the results obtained during tests on the first track (trial 1), it can be stated that during the ride on a narrow track there were no significant differences (under 10%) between solutions of camera configurations. The shortest mean time duration was achieved in configuration 3, which was 92 s, but it was only 4% shorter than configuration 2 and 7% shorter than configuration 1 (Figure 5). It should be noted that the average time needed to complete test 1 for tested configurations in comparison to the time of the reference test with direct observation of the surrounding area (52 s) increased by 90% (configuration 1), 84% (configuration 2), and 77% (configuration 3). The average time of task 1 execution (Figure 6) was 38 s (configuration 1), 39 s (configuration 2), and 37 s (configuration 3). The differences in the values did not exceed 5%. The time of task 1 execution for direct observation of the surrounding area was 46-51% shorter in comparison to the tested camera configurations. When cornering (task 2), again the shortest average travel time (Figure 7) was achieved for configuration 3 (26 s). In task 2, the differences in drive times between configurations are significant and exceed 20%. Task 2 execution time for direct observation of the surrounding area was 46-56% shorter than the tested camera configurations. When departing to an open space (task 3), the shortest average travel time (Figure 8) was obtained for configuration 2 (25 s), which in previous tasks turned out to be the worst. In task 2, the differences in average drive times between configurations are significant and reach almost 30%. Task 3 execution time for direct observation of the surrounding area was 24-41% shorter than the tested camera configurations. The number of lane violations per ride (control errors) shows that there are much bigger differences in how well teleoperations work for different camera configurations. It clearly indicates that the best solution is configuration 3 (Figure 9). In this case, the average number of errors per ride (0.26 error per ride) is 60% lower than in the worst solution, which is configuration 2. Compared to configuration 2, configuration 1 reduces the number of lane violations by only 32%. It should be noted that the vast majority of lane violations in all configurations occurred during tight corners. Figure 8: Departure time to the open space for 3 camera configurations and driving with direct observation of the surrounding—trial 1—task 3. Analyzing travel times, it can be concluded that configuration 2 enabled the fastest travel while driving without the need to precisely avoid obstacles (task 3). Task 3's execution time was only 32% longer than during direct observation of the surrounding area. However, during precise navigation between obstacles (task 2), configuration 2 turned out to be less effective. A comparison between the average execution time of task 1 and task 3 (20 m long section) reveals a significant increase in task 1's duration compared to task 3 (19% for configuration 1, 56% for configuration 2, and 24% for configuration 3). This means that teleoperation efficiency is relatively low during tasks that require precise assessment of UGV position and obstacles.