# Explaining Deep Learning-Based Driver Models
## 2 Background and Related Work
### Driver Behavior and Distraction Recognition  
Advanced Driver Assistance Systems (ADASs) are systems that assist the driver in order to increase safety in the car and, more generally, on the road. In Europe, in 1986, different automobile companies and research institutes worked on the Prometheus project (Programme for a European traffic of highest efficiency and unprecedented safety), which proposed solutions, especially to traffic problems [8]. However, at that time, the required technology was not sufficiently mature, and it has only been in the last two decades that this type of research has made important advances. Currently, research has focused on the development of an ADAS capable of intervening in various ways in order to avoid potential danger [8] and thus increasing vehicle safety [9]. In this sense, since 94% of traffic accidents are caused by human error [10], research in all areas related to ADAS development is essential. Driver-based ADASs are those that incorporate information obtained from the driver to provide any kind of assistance [11]. The incorporation of this type of driver information is crucial; for example, a lane departure detection system that does not integrate driver information cannot detect whether the vehicle departure is intentional or an error. Thus, if the driver is being monitored and drowsiness is detected, it can be deduced that the vehicle departure is unintentional, and some kind of warning could be triggered. For this reason, behavior driver monitoring is a key aspect of driver assistance. Thus, one of the reasons for the importance of driver modeling is the development of technologies related to driver safety so that they can be incorporated into an ADAS. However, in addition, driver modeling is important because it allows for the emulation of human behavior [12] in the development of autonomous vehicles and because it will allow us to understand how humans behave when driving on the road with autonomous cars [13]. Driver behavior modeling can be classified as reactive or predictive [14]. Reactive models consider the driver's behavior after the action has already been performed. For example, driver training systems employ reactive models to identify danger situationsgenerated by the driver. On the other hand, predictive models are more complex since they need to identify the driver's actions in real time in order to assist the driver in dangerous situations. A wide variety of models have been proposed and developed to predict, among other aspects, driving maneuvers [15; 16], driver actions [17], driver intentions [18], driver states [19], and driver emotions [20]. In relation to the detection of distractions, it is important to take into account that a distraction, unlike inattention, is related to a certain activity (e.g., talking on the phone or turning on the radio) that affects the driver's attention but is not related to the driver's state [13]. In [21], different deep learning-based methods to classify driver's distractions (such as texting, talking on the phone, operating the radio, drinking, and reaching behind) using data from 2D cameras are compared. In [22], the use of data augmentation and Convolutional Neural Networks (CNNs) are shown to be effective at the recognition of distractions and improving classification results, while also reducing training time. With the same purpose, some techniques based on tracking the driver's gaze and attention using head position are proposed in [23]. The result of training a CNN (named HandyNet), which is capable of detecting, segmenting, and localizing (in 3D) driver hands inside a vehicle cabin, is proposed in [24]. In that research, it is used segmenting and tracking hands through the use of depth images and annotation based on chroma-keying. Using more complex setups, in [25], whether prefrontal brain region electroencephalography (EEG) can be used to detect driver's fatigue is examined.