## 1 Introduction  
According to the latest estimates by the World Health Organization, approximately 1.35 million people die each year from road traffic accidents and between 20-50 million people suffer from non-fatal injuries [1]. Advanced driver-assistance systems (ADAS) are designed to support humans during the driving process, leading to an increase in road safety. Conventional ADAS technologies are mainly based on controlling the vehicle state through proprioceptive (i.e., Odometry, inertial sensors) and exteroceptive sensors (i.e., Lidar, vision sensors, radar, infrared, and ultrasonic sensors) [2]. These state-of-the-art technologies allow for the recognition of objects [3], alerting the driver about dangerous road conditions [4], providing driver tips to improve their driving comfort and safety [5], recognizing traffic activity and behavior [6], and detecting risky driving conditions [7]. In addition to factors currently evaluated by ADAS technologies, it is also of fundamental importance to monitor the driver's psycho-physiological state that is strictly related to driving performance as reported by National Highway Traffic Safety Administration (NHTSA) [8]. According to the latest estimation by the NHTSA [8], in the USA, more than 2800 people died and approximately 400,000 people were injured in crashes induced by distracted driving in 2018. In particular, driver drowsiness/fatigue and emotion (i.e., visible anger, sadness, crying, and/or emotional agitation) can increase the risk of car accidents by 3.4 and 9.8 folds, respectively [9]. Driver state monitoring is mainly based on two categories of approaches that depend on the nature of the data collected [10]. The first approach, named the behavioral method, is based on monitoring a driver's parameters including gaze direction, blinking frequency, percentage of eye closure (PERCLOS), yawning, and head pose. These parameters are evaluated by means of one or multiple visible cameras. This procedure, based on cameras, is indeed contactless and non-invasive, but it is characterized by relevant technical challenges derived from occlusion, illumination variation, and personal privacy issues. Nonetheless, because of its utility, it is often employed in the automobile industry. The second approach, labeled the physiological method, is instead based on monitoring driver's vital signals, such as those derived from electrocardiogram (ECG) [11], photoplethysmography (PPG) [12], electrooculography (EOG) [13], electroencephalography (EEG) [14], galvanic skin response (GSR) [15], and electromyography (EMG) [12]. With focus on driver stress, it is known that a high stress state decreases decision-making capabilities and situational awareness, impairing driving performance [16]. Electroencephalography has been widely employed to monitor drivers' stress. In particular, human stress can be inferred by measuring an increase in heart rate as well as by measuring variations of parameters associated to heart rate variability (HRV) which, in turn, can dynamically reflect the accumulation of mental workload [17]. Among the variety of indices derived from ECG signals, [PERSON] [18] proposed the Stress Index (SI) which is indicative of both sympathetic activity and central regulation. Although the SI is a sensitive and specific metric to stress, it is based on contact-based technology (e.g., ECG). Indeed, to ensure comfort and non-intrusiveness for drivers, the use of contact sensors for data collection should preferably be avoided. Infrared (IR) or thermal imaging is a passive technology able to evaluate the spontaneous emission of body thermal energy and measure the temperature in a contactless manner. Infrared imaging indeed allows to overcome the limitations of contact devices and, importantly, in comparison with visible cameras, is not affected by illumination and can work in a completely dark environment.