# Public Perception of the Introduction of Autonomous Vehicles
## 3 Results
Finally, if we calculate a cumulative percentage of preferred levels, given in Table 3, from level 2--partial automations with active safety features included--to level 5, we obtain a result of 82.41%; this is very similar to the results obtained by the other survey. In line with previous questions, respondents were asked to report when they were planning to buy an AV and give their opinions on the timeline for the transition to AV technologies in KSA. Half of the respondents (50%) stated that they plan to buy an AV in 10 years, whereas more than 40% indicated a prediction that 2030 would be the transition year for AVs, as shown on Figure 6. As expected, the year 2030 is aligned with the SA government's long-term planning for the introduction of intelligent transport and smart cities. \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Automation Level**} & **Preferred Level** & **Willing to Pay** \\  & \(\mu_{0}\leq\textit{Level 2}\), \(\mu_{1}>\textit{Level 2}\) & \(\mu_{0}\leq\textit{Level 2}\), \(\mu_{1}>\textit{Level 2}\) \\  & _t_**-Test, _p_-Value = 1.56 \(\times\) 10\({}^{-12}\)** & _t_**-Test, _p_-Value = 9.9 \(\times\) 10\({}^{-11}\)** \\ \hline level 0 & 9.26\% & 7.41\% \\ \hline level 1 & 8.33\% & 9.26\% \\ \hline level 2 & 12.04\% & 16.67\% \\ \hline level 3 & 20.37\% & 24.07\% \\ \hline level 4 & 20.37\% & 36.11\% \\ \hline level 5 & 29.63\% & 6.48\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The level of AV acceptance data. Figure 6: Percentage of responses to Q11 and Q12, i.e., timeline to buy an AV. Participants were asked questions about the benefits and risks they predict in the transition to increased use of AVs. Based on Table 4, most had a positive attitude. When collapsed summaries (high responses versus low responses) were calculated, 51.86% of respondents answered that there would be significant benefits associated with the transition to increased use of AVs. A total of 48.15% answered that there would be minimal risks associated with the transition. There were very close percentages of respondents who answered this question by saying they predicted average benefits and risks would accompany the transition to increased use of AVs--34.26% and 39.81%, respectively. Comparatively, previous research [23; 24; 25; 26] has found that gender might impact consumers' perceptions. Thus, we include Figures 7 and 8 to display gender comparisons: a bar chart and a box plot along, with an independent samples \(t\)-test. In terms of general opinions, there was a clear trend observed among women and men who are more likely to have positive attitudes toward AVs. However, a between-groups \(t\)-test showed that significantly more women would expect benefits from this transition than men (t = 2.0629, \(p\) = 0.04817), whereas only slightly more women than men would expect fewer risks (t = \(-\)1.9295, \(p\) = 0.062  
Model Building in R_  
Ordinal logistic regression was performed with the perceived benefits of the transition toward increased use of AVs as the dependent variable, with six predictors (gender, age, education level, stakeholders' groups, management role level, and prior knowledge). To fit the OLR model, transformation was necessary for some variables to prevent the possibility that the results of our research could be influenced by a small proportion of observations that appeared in some categories. For the gender variable, women were coded as 0 and men were coded as 1. The age variable was converted as follows: 0 = 35 or below 35, 1 = older than 35. The variable for the level of education was recoded: 0 = graduate degree or lower, 1 = postgraduate degree. The transport mode was excluded from the model to avoid misanalysis. For the stakeholder group variable, responses from the government group were coded as 0, responses from the industry group were coded as 1, and responses from the general public were coded as 2. Note that the traffic authority group was excluded, as no responses were recorded.