## 1 Introduction  
Understanding human behavior is on the epicenter of modern AI research. Modeling and monitoring a user's state is critical towards designing adaptive and personalized interactions and has lead to ground-braking changes on several domains during the last few years. The transportation sector, in particular, is one of the application areas that have invested the most in \"smart\" monitoring with the broader goal of increasing safety and improving the quality of the overall experience [1]. That is especially true for the automotive industry, as for many years the number of road accidents has been steadily increasing and car manufacturers have shifted their attention on the search of machine learning and AI-powered solutions. According to the World Health Organization (WHO), each year 1.35 million people lose their lives to road accidents while 50 million get injured [2, 3]. That translates to approximately 3700 deaths and 137,000 injuries daily. Moreover, based on the same resource, road traffic injuries are the leading cause of death for children and young adults between the ages of 5 to 29 years. Particularly, young males are three times more likely to be involved in a car accident than young females, with mobile phone usage being the most common cause of distractions. What is especially surprising according to WHO findings, is that hands-free phone usage remains almost equally dangerous to the physical interaction with the device. It is estimated that road crashes cost most countries an average of 3% of their gross domestic product while future trends show that by 2030, fatalities related to road accidents will be the fifth most common cause of mortality globally, from being ninth in 2011. Specifically in the US, the National Highway Traffic Safety Administration (NHTSA) reports that only in 2018, 2800 lives were lost and more than 400,000 people were injured due to distracted driving. Additionally, only in 2017, 91,000 police-reported crashes involved drowsy drivers leading to an estimated 50,000 people injured and nearly 800 deaths. However, as the NHTSA suggests, there is broad agreement across the traffic safety, sleep science and public health communities that these numbers are an underestimate of the real impact that driving while being mentally or physically fatigued can have. An underestimate that occurs due to the lack of technology and tools to detect and account for drowsy driving behaviors [4; 5]. In this work we address the problem of driver state modeling with respect to both distraction and alertness. The originality of our work stems from two main stand points. First, this is one of the very few efforts to tackle both conditions in parallel and study how they intersect. Second, in this study we focus explicitly on four different types of physiological markers; blood volume pulse, skin conductance, skin temperature and respiration. That is in contrast to the vast majority of driver monitoring systems that exploit either visual-based information such as facial and motion analytics [6; 7; 8] or vehicular-based data such as miles per hour, steering patterns, etc. [9; 10; 11]. The largest portion of studies that research physiological signals for driver behavior modeling, focuses on detecting and measuring stress [12; 13; 14]; a condition that may have a latent relation with both distraction and drowsiness but is by no means identical to either of them. That is a general truth but also holds specifically under the context of driving as confirmed by [PERSON] et al. [15]. **Through our experimental analysis we try to answer three main questions which also summarize the scientific contribution of this work:**  
1. Which physiological indicators are most indicative of drowsy and distracted behavior? 2. Are there specific statistical features coming from different signals that are particularly informative? 3. Is it possible to jointly tackle the problems of drowsiness and distraction detection and how such a framework can be formulated?