*Contribution*

Most of the works documented in the literature focus on ascertaining the emotions of the driver while driving. There are some studies in which the driver's previous state is analyzed, but they are minimal. Furthermore, they do not investigate how emotions together with factors such as the interior state of the vehicle (temperature, humidity and CO2 concentration) or the music the driver listens to affect driving and the level of stress. Another problem we have found in previous works is that they artificially induced moods, which could lead to inaccuracies in the results.

The objective of this work is to analyze which elements affect driving behavior and stress levels, focusing on the drivers' initial emotions, their characteristics and the comfort inside the car. The conclusions of this analysis can be used to include the emotional component in driving assistants. Most of them are limited to warning the driver when they invade the opposite lane or exceed the speed limit.

#### **2. Related Work on Stress Detection**

Stress is defined as a state of physical, psychological or emotional health experienced by a person when the perceived or actual demand requires a high number of resources [27]. Stress appears when the demand for mental workload exceeds the capabilities of the subject [28]. Stress may be accompanied by other emotions such as anxiety [29]. However, it is not always bad. Healey et al. [5] classified stress into two types: eustress and distress. Eustress encourages people to achieve a high level of performance. If the level of stress is too low, it can cause drivers to suffer fatigue and drowsiness, and they may lose control of the vehicle [30]. Distress appears when there is an excess in the level of demand that surpasses the capacity of the person and consequently discourages the driver [31]. The level of stress experienced while driving can be affected by four factors: the physical and mental condition of the driver, road and traffic conditions, vehicle condition and external disturbances. This paper focuses on the driver's mental state and vehicle condition (music tempo and CO2concentration).

Stress detection methods can be classified into four categories:


The self-report questionnaires analyze the driver's behavior and strategies for coping with different types of stressful events. In addition, the characteristics of the driver are very important [32]. Data such as age or accident history have a strong relationship with stress. Drivers who have suffered

more tra ffic accidents are more likely to feel anxiety and develop post-traumatic stress disorders [33]. One of the most used questionnaires in research is the driver behavior inventory (DBI) [34]. In this questionnaire, stress is defined by five elements: driving aggression, dislike of driving, tension and frustration connected with successful or unsuccessful overtaking, irritation when overtaken and heightened alertness and concentration. There are also many other questionnaires such as: the driving stress inventory (DSI) [35], stress arousal checklist (SACL) [36] and Dundee stress state questionnaire (DSSQ) [37]. In the case of workload measurement, NASA load index (NASA-TLX) [38] and driving activity load index (DALI) [39] are the most widely used. In the experiments, several of them can be used with di fferent objectives. For example, in [40], the participants completed the DSI questionnaire before the test to estimate their vulnerability to stress. They then completed the DSSQ questionnaire to analyze the stress and workload caused by the task.

Stress detection models based on physiological signals allow us to objectively monitor the driver's stress level in real time. They mainly use the heart rate signal, skin conductance, skin temperature and the encephalogram [41,42]. The main disadvantage of these methods is that they require the use of sensors, which increases the cost and reduces the number of potential participants. In addition, these solutions can cause discomfort if they are intrusive. However, in recent years, wearable devices have been developed that can monitor the driver without a ffecting mobility and at a relatively low cost. An example widely used in research is the Empatica E4 [43]. These portable devices are not as accurate as medical devices. However, there is a strong correlation between them, and they are valid for measuring stress and conducting long-term studies [44,45].

In the literature, we find many proposals of stress detection based on these types of signals. A strong relationship between driving stress and heart rate and blood pressure was reported in [32,46]. In [47], the authors proposed a binary logistic regression model to predict driving stress. This method uses galvanic skin response data obtained in real road driving situations to predict whether driver stress will be high or low. GSR data were collected using a wearable device (Empatica E4). The main advantage of this solution is that it is non-intrusive so it can be used in real driving. The authors achieved an accuracy higher than 80% using this model. In [48], the authors wanted to analyze the relationships between driving stress, tra ffic conditions and road types. The authors proposed using electrodermal activity (EDA) signals to estimate the levels of driving stress taking into account the road type and tra ffic conditions. The classification model developed was based on the data collected by a driver in real road driving conditions for 60 min a day for 21 days. The results showed than tra ffic conditions and road type are factors that influence driving stress.

Proposals using physiological signals can detect driver stress in real time using artificial intelligence algorithms [5,49]. Galvanic skin response (GSR) and heart rate variability (HRV) are considered the best indicators of stress in real time [5]. However, we should take into account the latency that in the case of skin conductivity can be up to 1.4 s [50].

A di fferent alternative to using these sensors is to analyze the driver's face and speech. This avoids having to wear sensors. For example, the authors in [51] used visual-based thermography to detect facial skin temperature. In [52], the authors proposed to analyze facial expression using an NIR camera. The drawback is that good illumination is required to achieve accuracy in stress detection. Voice speech is another variable that can be helpful for detecting stress. In [53], the authors analyzed the changes in pitch of the subjects to detect stress. The problem with this type of approach is that it requires the driver to perform additional tasks while driving in order to make the voice recording, which could cause distractions [54]. In addition, noise inside the vehicle cabin could make it di fficult to detect stress [55].

There are some proposals to detect stress based on driving behavior. The authors in [56] highlighted that the autonomic system (ANS) and driving style change when the level of stress is high. Stressful events can be detected by analyzing the corrections the driver makes with the steering wheel and the pedals of the vehicle. In [57], the researchers proposed a system that monitors the turning patterns of the steering wheel and recognizes lanes and accelerating patterns in order to detect stress.

Finally, there are proposals that combine the use of physiological signals with vehicle telemetry (steering wheel movement, acceleration, deceleration). In [58], the authors presented a wearable glove system for monitoring stress while driving. The proposal extracted features of photoplethysmography (PPG) and inertial measurement unit (IMU) sensors located in the glove to assess the stress events. The proposal was able to detect stress events with an accuracy rate of over 95% using an SFS-SVM classifier with the RBF kernel function. The main limitation of this device is that participants cannot change the position of their hands on the steering wheel during the driving test.

#### **3. Materials and Methods**

In this section, we will describe the materials and the procedure to carry out the experiment. We present the sensors (Figure 1) used to monitor driving stress, to evaluate driving performance and to obtain the state of the simulation environment (temperature, humidity and CO2 level). Driving stress is tracked using an Empatica E4 wristband and a Polar H10 chest band. The environment is supervised using the Netatmo device. We also define the measurements that we will use to evaluate the drivers and their driving behavior from the data gathered by the sensors. In addition, the test scenario will be detailed, explaining the simulator used, as well as the music that the driver listened to during the driving task. It will specify the survey completed by the drivers before and after the test to ascertain their characteristics, their opinion of the experiment and their physical and mental state.

**Figure 1.** Sensors used in the experiment.
