## 2 System Architecture
### DVE State Recognition and Monitoring System  
Introduction of DVE status recognition and monitoring technologies into automotive HMIs helps to improve the safety and reliability of vehicles by recognizing the status of driver, vehicle, and surrounding environment. In particular, recent rapid advances in intelligent computing platforms have promoted the development of complex multi-sensor computing systems for active perception of driver's status [48], including cognitive load [49], secondary activities [50], emotion [51], positions [52], fatigue [53, 54], etc. Figure 1: A-HMI system architecture concept. To totally assess danger of driving conditions, these systems usually estimate the risks related to traffic and environmental factors as well [55; 56]. An example of this advanced approach for DVE monitoring has been proposed by the AIDE (Adaptive Integrated Driver-vehicle interfacE) project (2004-2008) [37]. Here, a set of five modules, each of them designed to address a different dimension of the DVE state, i.e., Traffic and Environment Risk Assessment module, Driver Characteristic module, Driver Availability Estimator module, Driver State Degradation module, and Cockpit Activity Assessment module, derive a complete set of information (summarized in many parameters) about driver, vehicle, and environment conditions. In this study, the A-HMI includes a non-invasive and low-cost DVE state recognition and monitoring system that, by common in-vehicle sensors, computes in real-time a set of five DVE parameters needed to enable adaptive management functions of the A-HMI. These give an abstract discrete representation of the environment, drivers, and vehicle state perceived, which is a key driving factor to estimate drivers' ability to process actions requested by applications, which could be a potential source of distraction. The five DVE parameters are defined as follows. * DVE \(1\in\mathbb{B}=\{0,\,1\}\): driver's eyes off the road (0--No, driver is currently looking at the road ahead; 1--Yes, driver is currently looking at something other than the road ahead). Calculated by using driver head and eye movements and vehicle speed as input data, it gives a discrete representation of driver cognitive load or shift of visual attention away from the road ahead, induced by an external event or a secondary task. * DVE \(2\in\mathbb{B}=\{0,\,1\}\): driver impairment (0--Normal, alert and few driving errors; 1--Dangerous, drowsy with some driving errors, critical driving time, and long trip duration). Driver state of drowsiness is calculated based on lane positioning, driving time, and PERCLOS [57; 58; 59] (the percentage of eyelid closure over the pupil over time) detected by an in-cabin camera with a face-mesh technique. It describes the physical ability of the driver to drive (fatigue, sleepiness, etc.). * DVE \(3\in\mathbb{B}=\{0,\,1\}\): traffic risk (0--Low, no risk of collision with any other vehicle in path; 1--High, there is a risk of collision with other vehicles in path). This parameter represents the level of risk concerning the traffic density. It is driven by traffic risk established by Forward Collision Warning (FCW). Specifically, FCW warning sets the traffic risk to 1, otherwise it assumes value 0. * DVE \(4\in\mathbb{B}=\{0,\,1\}\): environment risk low visibility (0--Low, no risk associated with low environment visibility; 1--High, there is a risk for low environment visibility). Poor weather conditions (rain, fog, etc.) and unit roads detected from the vehicle's indicators and controls (e.g., wipers, rain detection sensor, fog lights, etc., and dipped headlights, high beams, etc., respectively), along with road and traffic attributes (e.g., high traffic density and high curvature of road until a certain distance ahead of the vehicle), are used to assess risk for low environment visibility [47]. At the current state of implementation, this parameter gives a discrete representation of the environmental conditions estimated only from wiper actuation state and rain detection sensor.