## 3 Results and Discussion  
The Shapiro-Wilk test was used to check the normality of the distribution. The distributions of the following parameters were analyzed:  
- The total time to complete trial 1 (\(t_{1}\));  
- The time to complete task 1 (\(t_{1,1}\));  
- The time to complete task 2(\(t_{1,2}\));  
- The time to complete task 3 (\(t_{1,3}\));  
- The total time to complete trial 2 (\(t_{2}\));  
- The number of lane violations on trial 1 (\(n_{1}\));  
- The number of lane violations on trial 2 (\(n_{2}\));  
- The number of hitting obstacles on trial 2 (\(n_{2,1}\));  
- The number of undetected obstacles on trial 2 (\(n_{2,2}\));  
- The number of unrecognized obstacles on trial 2 (\(n_{2,3}\)). In the first test (trial 1), normality of the distribution analysis was performed for three camera configurations and direct observation. For the second test (trial 2), normality of the distribution analysis was performed only for three camera configurations. The significance level \(\alpha=0.05\) was adopted. For 20 people tested according to the data in the tables of coefficients for the Shapiro-Wilk test, the critical value is \(W_{(0.05,\ 20)}=0.905\). The following results were obtained in Table 1, where W--Shapiro-Wilk test indicator, p--the probability of occurrence of the assumed type I error [51]. The results of the normality of the distribution analysis (Table 1) indicate that not all parameters are characterized by a normal distribution (W < 0.905). Therefore, two separate statistical analyses were performed:  
- ANOVA [52]--for parameters characterized by a normal distribution (\(t_{1}\);\(t_{1,2}\);\(t_{1,3}\); \(t_{2}\)), to demonstrate sufficient test power to demonstrate a relationship between camera configurations and complete trial time;  
- Kruskal-Wallis [53]--for parameters that do not have a normal distribution (\(n_{1}\); \(n_{2}\); \(n_{2,1}\); \(n_{2,2}\); \(n_{2,3}\)), to check the significance of the relationship between the number of errors made and the camera configurations. The critical value of the Kruskal-Wallis test--\(H\) = 5.9914, for \(\alpha=0.05\) and \(df=2\) (the degrees of freedom is number of groups minus one). Based on the conducted ANOVA analysis, it was found that the number of trials is sufficient to demonstrate with a test power of over 80% that the time to complete a trial depends on the camera configurations:* For trial 1--t\({}_{1}\) (\(\alpha\) = 0.05 and RMSSE (Root Mean Square Standardized Effect) = 0.161698);
* For task 1--t\({}_{1}\) (\(\alpha\) = 0.05 and RMSSE = 0.159098);
* For task 2--t\({}_{1}\)_2 (\(\alpha\) = 0.05 and RMSSE = 0.293171);
* For task 3--t\({}_{1}\)_2 (\(\alpha\) = 0.05 and RMSSE = 0.465155);
* For trial 2--t\({}_{2}\) (\(\alpha\) = 0.05 and RMSSE = 0.69969). Kruskal-Wallis analysis showed that there is a statistically significant relationship between camera configuration and the following:  
* The number of lane violations on trial 1--_n\({}_{1}\)_(\(\alpha\) = 0.05; \(df\) = 2; \(H\) = 12.33742; \(p\) = 0.0021)
* The number of lane violations on trial 2--_n\({}_{2}\)_(\(\alpha\) = 0.05; \(df\) = 2; \(H\) = 6.261478; \(p\) = 0.0058);
* The number of hitting obstacles on trial 2--_n\({}_{2,1}\)_(\(\alpha\) = 0.05; \(df\) = 2; \(H\) = 47.59081; \(p\) = 0.00001)
* The number of undetected obstacles on trial 2--_n\({}_{2,2}\)_(\(\alpha\) = 0.05; \(df\) = 2; \(H\) = 96.78267; \(p\) = 0.00001)
* The number of unrecognized obstacles on trial 2--_n\({}_{2,3}\)_(\(\alpha\) = 0.05; \(df\) = 2; \(H\) = 95.40297; \(p\) = 0.00001)
* The data number for the Kruskal-Wallis analysis--N was 600. The test results of the times needed to complete trial 1 and the tasks performed within trial 1 (tasks 1-3) depending on the camera configurations and for the direct observation surrounding are presented in Figures 5-8. They used a box plot to show the distribution of a set of data.