**4. Results**

#### *4.1. E*ff*ects of Initial Stress*

The drivers have been grouped into two sets, "stressed" and "non-stressed", according to the initial level of stress. The drivers indicated their stress level using a Likert scale with values between 1 and 5, where 1 means that they are not su ffering from stress and 5 that they have a lot of stress. The "stressed" group is made up of 21 drivers. These indicated in the initial survey that their stress level was equal to or higher than 4. The "non-stressed" group consists of 29 drivers. These drivers indicated a stress level equal to or less than 3. In order to analyze if there are significant di fferences between the two groups, we conducted a Student's test or a Wilcoxon's test for independent samples, depending on whether or not the normality hypothesis is verified. We use *p* < 0.05 as the significance level.

Table 6 shows the variables related to stress during driving. The participants who initially indicated that they had stress also obtained values associated with high stress during the driving test. We have found significant di fferences in two of the three variables analyzed. The result of Wilcoxon´s test is Z = −3.116, *p* < 0.05 for pNN50, Z = −3.803, *p* < 0.05 for LF/HF ratio and Z = −3.491, *p* < 0.05 for SCR amplitude.


Stress also has consequences on driving behavior. Stressed drivers accelerate and brake more frequently and intensively than other drivers, as can be seen in Table 7. The di fference in driving behavior is especially important in harsh accelerations and decelerations. The percentage of sudden accelerations is six times higher compared to unstressed drivers and twice as high in the case of sudden braking. The result of Wilcoxon´s test is Z = −5.376, *p* < 0.05 for harsh braking, Z = −2.428, *p* < 0.05 for braking time and Z = −5.063, *p* < 0.05 for harsh acceleration. In the case of the acceleration time, neither the normality hypothesis nor the equality hypothesis of variances are rejected. Therefore, we carry out a Student's test whose result is t(48) = 2.703, *p* < 0.05.

Figure 2 shows the degree of compliance with tra ffic rules grouped by initial stress level. We have found significant di fferences between stressed drivers and non-stressed drivers in "Speed limit exceeded" (Z = −5.184, *p* < 0.05), "Do not yield to a pedestrian in a crosswalk" (Z = −2.695, *p* < 0.05) and "Crossing the lane markings illegally" (Z = −2.588, *p* <0.05). Drivers who are initially stressed often drive at high speed, invade the opposite lane to overtake other vehicles and do not stop at crosswalks.


**Table 7.** Driving behavior grouped by initial stress level.

**Figure 2.** Average number of traffic rules broken grouped by initial stress level: (**a**) Speeding; (**b**) Do not yield to a pedestrian at a crosswalk; and (**c**) Crossing the lane markings illegally.

Figure 3 compares the difference between initial and final fatigue for the two groups of drivers. These values were obtained from the pre-test and post-test surveys. On the one hand, stressed drivers suffer an important increase in the fatigue level after completing the driving test. The tiredness grew by 20%. In the case of drivers with low initial stress, the level of tiredness scarcely changed. On the other hand, at the beginning of the driving experiment, we found no significant differences in the fatigue level between the two groups of drivers analyzed. The result of Wilcoxon´s test is Z = −1.491, *p* > 0.05. However, we observed significant differences at the end of the experiment. The result of Wilcoxon´s test is Z = −4.545, *p* < 0.05.

**Figure 3.** Fatigue evolution grouped by initial stress level.

#### *4.2. E*ff*ects of Sadness*

The drivers have been grouped into two sets according to the sadness level. The drivers indicated their sadness level using a Likert scale with values between 1 and 5, where 1 means that they are very happy and 5 indicates that they are very sad. The group of drivers with sadness is composed of 17 drivers. These indicated in the initial survey that their sadness level was equal to or higher than 4. The non-sadness group is formed by 33 drivers who rated their level of unhappiness with a value equal to or less than 3.

Table 8 shows the variables related to stress during driving. Drivers who show sadness are also those who have a higher level of stress. However, the differences are not significant. The result of Wilcoxon´s test is Z = −0.881, *p* > 0.05 for pNN50, Z = −0,522, *p* > 0.05 for LF/HF and Z = −0.420, *p* > 0.05 for SCR amplitude.

Table 9 presents the acceleration and deceleration values obtained by the drivers. We observe that drivers with sadness accelerate sharply more times than drivers without sadness, although the difference is not significant. The result of the Student's test is t(48) = 2.001, *p* > 0.05. No significant differences were found either in the rest of the parameters.



**Table 9.** Driving behavior grouped by sadness level.


Figure 4 captures the average number of traffic accidents. Drivers with sadness suffer traffic accidents more often than the group of drivers without sadness. The difference between the two groups is especially relevant, as the group with sadness is involved in four times as many accidents as the group of drivers without sadness. The result of Wilcoxon´s test is Z = −4.741, *p* < 0.05.

Figure 5 compares the level of fatigue before and after the driving test. Drivers suffering from sadness increased their fatigue level by 11.5% compared to their initial value. In the case of drivers without sadness, fatigue increased by 7.5%. However, we found no significant differences between both groups at the beginning and at the end of the experiment. On the one hand, the result of Wilcoxon´s test in the initial survey is Z = −0.361, *p* > 0.05. On the other hand, the result of Wilcoxon´s test in the post-experimental survey is Z = −1.472, *p* > 0.05.

**Figure 4.** Number of traffic accidents grouped by sadness level.

**Figure 5.** Fatigue evolution grouped by sadness level.

#### *4.3. E*ff*ects of Fatigue*

In order to analyze this factor, we have divided the samples into two groups. The drivers indicated their initial fatigue level using a Likert scale with values between 1 and 5, where 1 means that they are very vigorous and 5 indicates that they are very tired. The non-fatigue group consists of 36 drivers. These drivers showed a tiredness level equal to or less than 3. The fatigue group is made up of 14 drivers. These indicated in the initial survey that their fatigue level was equal to or higher than 4.

Table 10 reveals the average stress level during the test grouped by the initial fatigue level. The results indicate that tired drivers suffer more stress while driving than the other drivers. The variable pNN50 is eight times lower in the group of drivers who are tired, and the LF/HF ratio and SCR amplitude are twice as high. Low values of pNN50 and high values of LF/HF ratio and SCR amplitude

are correlated with high stress. In all variables, the differences are significant. The result of Wilcoxon´s test is Z = −4.905, *p* < 0.05 for pNN50, Z = −4.127, *p* < 0.05 for LF/HF and Z= −3.297 for SCR.


**Table 10.** Heart rate variability and skin conductivity during driving test grouped by tiredness level.

Driving behavior is also affected by this state. Table 11 shows the use of the accelerator and brake. Acceleration time and braking time is higher for tired drivers than for rested drivers. The differences are significant. The result of the Student's test is t(48) = 2.905, *p* < 0.05 for acceleration time and t(48) = 3.754 *p* < 0.05 for braking time. This means that the drivers are continuously making speed corrections and increasing fuel consumption. No significant differences have been found in the case of abrupt maneuvers, although both average and median values are higher for tired drivers.

**Table 11.** Driving behavior grouped by level of tiredness.


Figure 6 captures the number of broken driving rules in which there are significant differences between fatigued and non-fatigued drivers. The result of Wilcoxon´s test is Z = −4.402, *p* < 0.05 for "Stopping over the crosswalk" and Z = −3.459, *p* < 0.05 for "Do not yield to a pedestrian at a crosswalk". Tired drivers stop over the crosswalk 4.5 more times more than the rest of the drivers. Furthermore, they did not yield to a pedestrian at a crosswalk two times more. This could increase the likelihood of running over a pedestrian.

**Figure 6.** Average number of traffic rules broken grouped by tiredness level: (**a**) Stopping over the crosswalk; and (**b**) Do not yield to a pedestrian at a crosswalk.

Figure 7 compares the difference between initial and final fatigue for the two groups of drivers. On the one hand, there is a significant increase in the fatigue level of the non-tired drivers. Tiredness increased by 13.79% after completing the driving test. On the other hand, in the case of tired drivers, the average fatigue value decreases by 8.39%. This could be because for some participants, the driving test is like a leisure activity. Despite this, the level of fatigue manifested by the drivers who were initially tired remains significantly higher than that of the drivers who initially did not feel tired. The result of Wilcoxon´s test is Z = −3.105, *p* < 0.05.

**Figure 7.** Fatigue evolution.

#### *4.4. E*ff*ects of CO2 Concentration*

In order to analyze this factor, we have divided the samples into two groups. One group consists of 29 drivers who drove with an average CO2 value of less than 1400 ppm. We have chosen this threshold because it has been shown in many articles [11] that di fferences in cognitive performance appear above this value. The average value of CO2 concentration of this group was 319.67 ppm (max: 562.55 ppm, min: 149.8 ppm, std. dev: 119.17 ppm). The second group is made up of 21 drivers who drove with an average CO2 value equal to or higher than 1400 ppm. The average value of CO2 concentration of this group was 1572.96 ppm (max: 1734.56 ppm, min: 1434.81 ppm, std. dev: 107.43 ppm). The average temperature value during all the tests was 25.27 ◦C (maximum = 26.71 ◦C, minimum = 24.12 ◦C, standard deviation = 0.63 ◦C) and the average humidity was 50.64% (maximum = 58.13%, minimum = 48.35%, standard deviation = 3.11%).

Table 12 captures the value of the variables associated with stress. The di fference between groups is not significant. The result of Wilcoxon's test is Z = −0.147, *p* > 0.05 for pNN50, Z = −0.88, *p* > 0.05 for LF/HF and Z = −0.364, *p* > 0.05 for SCR amplitude.


**Table 12.** Heart rate variability and skin conductivity during driving test grouped by CO2 level.

Table 13 shows driving behavior. The results indicate that the driver brakes more frequently when the passenger compartment has a high concentration of CO2. The di fference is significant between the two groups (high and low CO2 level). The result of Wilcoxon´s test was Z = −3.843, *p* < 0.05 for braking time. A high CO2 concentration causes drowsiness and a lack of concentration. The participant's cognitive capacity is reduced and he or she responds more slowly to events that happen on the road.

Consequently, as we can see in Figure 8, the driver violates more tra ffic regulations and is involved in a higher number of tra ffic accidents. We found significant di fferences in "Crossing the lane markings illegally" (Z = −2.478, *p* < 0.05), "Not stopping at a red light" (Z = −2.752, *p* < 0.05) and "Tra ffic accidents" (Z = −2.105, *p* < 0.05). Figure 8 captures the tra ffic rules broken and tra ffic accidents grouped by CO2 level. We can see how drivers who are exposed to high concentrations of CO2 invade the opposite lane 95% more than the rest of the drivers. Moreover, they respect tra ffic lights less. The group of drivers who drive with a high concentration of CO2 ignored the red lights 1.14 times on average, while the drivers who drive with a low CO2 level passed red lights 0.59 times. Frequent decelerations along with non-compliance with tra ffic regulations result in a sharp increase in the number of accidents of the group with the high CO2 concentration. These drivers su ffer 1.87 times more accidents than the rest of the drivers.


**Table 13.** Driving behavior grouped by CO2 level.

 **Figure 8.** (**a**) Number of times drivers cross the lane markings illegally; (**b**) number of times that drivers do not stop at a red light; and (**c**) number of traffic accidents.

Figure 9 captures the initial and final level of fatigue for each group of drivers. The results show that when the CO2 concentration is high, the fatigue level increases by 12% compared to the initial value, while when the CO2 level is low, the level of fatigue does not change.

**Figure 9.** Evolution of fatigue grouped by CO2 level.

#### *4.5. E*ff*ects of Music Tempo*

As in the previous analyses, the driving samples were divided into two groups. One group is made up of 23 drivers who listened to slow tempo music. The other group consists of 27 participants, but in this case the music was fast tempo music.

Table 14 captures the value of the variables related to stress. In the case of drivers listening to fast-paced music, pnn50 and LF/HF ratio are higher than drivers listening to slow music, although the differences are not significant. The result of Wilcoxon's test is Z = −0.049, *p* > 0.05 for pNN50, Z = −0.457, *p* > 0.05 for LF/HF and Z = −0.886, *p* > 0.05 for SCR amplitude.



Table 15 captures driving behavior grouped by music tempo. We observed that the average values of the four variables analyzed are higher in the case of drivers who listen to fast-paced music than participants who listen to slow-paced music. This means that drivers with fast-paced music show a more aggressive driving style, although we have only found significant differences in acceleration time. The result of the Student's test is t(48) = −2.891, *p* < 0.05. Likewise, we have found significant differences in the violation of speed limits, as can be seen in Figure 10. The result of Wilcoxon´s test is Z = −1.980, *p* < 0.05. As future work, we want to conduct more experiments to verify whether the differences between the driving behavior variables are significant if the number of participants is increased.


**Table 15.** Driving behavior grouped by music tempo.

**Figure 10.** Number of times the driver exceeds the speed limit.

Figure 11 shows the level of initial and final fatigue for the two groups of drivers using a Likert scale, where 1 means no fatigue and 5 a lot of fatigue. The level of fatigue only increased by 2.8% for drivers who listened to music at a slow pace. In contrast, drivers who listened to fast-paced music suffered a significant increase in the level of fatigue (by 7.5%). These results are consistent with those obtained by [95]. In this study, fast music deteriorated the level of fatigue.

**Figure 11.** Fatigue evolution grouped by music tempo.
