# Environmental-Based Speed Recommendation for Future Smart Cars +
## 4 Experimental Results
### Evaluation Scenarios  
#### 4.3.1 Urban Driving Scenario  
In Scenario 1, a test vehicle was used around the Carbondale, IL, USA area. Figure 5 illustrates the data we acquired utilizing only static on-route information in order to estimate the desired speed. The blue line depicts the speed of the vehicle during the experiment, whereas the red line reflects the recommended speed that the on-route information indicates, based on the extracted PoIs. However, on-route estimation depends on the starting and ending point of the trip and does not take into account the possible dynamic changes of the surrounding environment (weather conditions, actual road surface status).  
Figure 4: An instance of the classification tree with majority set to 95% and depth equal to 5.  
Figure 5: On-route based speed estimation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Predicted**} \\ \hline \multirow{4}{*}{**Actual**} & **Dry** & **Dry** & **Wet** & **Snow** & **Ice** \\  & **Dry** & 90.2\% & 4.0\% & 1.4\% & 1.7\% \\  & **Wet** & 6.8\% & 90.2\% & 4.7\% & 6.7\% \\  & **Snow** & 1.1\% & 2.2\% & 82.0\% & 16.3\% \\  & **Ice** & 1.8\% & 3.6\% & 11.9\% & 75.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.  
In Scenario 2, we replicated the same urban driving scenario, but we integrated the road class estimation based on the performed weather classification analysis. The blue line in Figure 5(a) represents the RSI of the car, based on its speed, assuming dry road conditions, while the red line represents the RSI of the car on the same wet road surface. Based on RSI definition [41], the higher the RSI, the better traction is achieved between the road surface and the car. In that sense, when RSI has a low value (usually in snow-ice road classes), it is more likely that the vehicle could slide on the road with unpredictable consequences. Thus, the purpose of the proposed methodology is that the car should achieve the optimum RSI value, adjusting its speed accordingly. Therefore, the green line in Figure 5(a) represents the targeted RSI, which is the maximum RSI that the vehicle can achieve, given the current wet road conditions. In order to achieve that maximum RSI value, the car should reduce its speed. Accordingly, the blue line in Figure 5(b) depicts the current speed of the car and the red line represents the speed that the car should adapt to in order to maintain optimum RSI under current wet road conditions.  
We also conducted another experiment on a hybrid driving scenario that included highway road segment and urban driving. Figure 6(a) presents the RSI estimation for the hybrid driving scenario. In particular, the blue line depicts the RSI under dry conditions. The red line represents the RSI under wet road conditions. The green line reflects the targeted RSI, which is the maximum RSI value that can be achieved under wet road surface status. Accordingly, Figure 6(b) illustrates the recommended speed that the car should adapt to, so that it will achieve the targeted RSI (Figure 6(a)). In Figure 6(a), it is observed that RSI estimation under wet road status (red line) has much lower values than the RSI under wet road status for a pure urban driving scenario (Figure 5(a)). That difference can be explained because of the speed differences between the two scenarios. In fact, the hybrid scenario includes a significant portion of highway segments with an average speed as high as 73 km/h, whereas the average speed in the urban driving scenario barely reaches 30 km/h.  
Figure 6: Urban driving scenario.  
Figure 7: Highway driving scenario.