# Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles  
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Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Vol. 1, pp. 1-1, 2010-2, 2010,  
dynamically (and, usually, unpredictably) arise while driving. For example, an eventual unsteady directional motion of the vehicle or even simple movements of the head or body of the driver could significantly alter the obtained signals. Moreover, as the researcher investigating these approaches acknowledges, the interference of simple actions such as chewing, raising eyebrows, or eye blinking will be prevalent in real driving. Also, we would like to emphasize both the inconvenience and questionable practical applicability of these approaches: the contemporary implementations of mobile EEG and EMG do not allow for convenient, easy, or quick setup. In another study [10], the authors built a framework that allows for the assessment of both the degree of criticality of the current road traffic situation and the need for intervention by an intelligent driving system. The proposed framework is based on the processing of several parameters obtained from the sensors of the vehicle via the controller area network (CAN). These parameters include the displacement time of the driver's foot (e.g., the time needed to move the foot from the accelerator pedal to the brake pedal), and head and facial movements. An alternative approach [11] takes into account the speed of releasing the accelerator pedal, foot displacement, and the speed of the brake pedal in order to detect emergency braking situations. The current industrial systems, however, from the whole set of parameters that could, in principle, characterize the behavior of the driver during emergency braking situations, consider only the motion pattern of the pressed brake pedal [12]. These systems are intended to support drivers who apply the brake pedal quickly but not strongly enough by applying the maximum force to the brakes in emergency situations. The behavior of the driver in such emergency braking situations was extensively explored in [13]. In the latter studies, the authors also proposed both a brake-force-based and a speech-based approach for identifying hazardous situations on the roads. Without any doubt, in cases of emergency braking, the aforementioned approaches are able to reduce the driver's response time and, as a consequence, the braking distance of the car. In none of these studies, however, was the motion pattern of the (lifting) accelerator pedal considered as the sole information needed for the reliable detection of the emergency braking situations. These facts motivated us to investigate the feasibility of predicting the emergency braking situation even before the driver presses the brake pedal (as implemented by most of the current industrial brake assistant systems)--namely, from the pattern of lifting the accelerator pedal [14]. In the case of detecting the special pattern--pertinent to the emergency braking situation--of the motion of accelerator pedal, the proposed system should be able to automatically activate the brakes well before the driver would have been able to apply them. As shown in Figure 1, different driving situations--such as accelerating, cruising, slowing down, normal braking, and emergency braking--exhibit different, specific motion of the accelerator pedal. We obtained the data illustrated in Figure 1 from the modelled parametric data recorder of a car driven by a sample human driver in a full-scale Forum-8 drive simulator [15], as shown in Figure 2. The simulator realistically models a real-world car featuring an automatic transmission, which implies that the driver lifts the accelerator pedal (Figure 1c-e) only when they really intend to reduce the speed of the car (or, ultimately, to stop it).