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A significant amount of research has been involved with the development of advanced driver-assistance systems. Such systems typically include radars, laser or video sensors that detect the vehicle trajectory and warn for an imminent lane departure, or sense the front vehicle's speed and apply the brakes of the following vehicle to maintain safe distance headways (i.e., collision avoidance system). However, most of these systems rely on the subject vehicle and surrounding vehicles' position and do not explicitly consider the driver's actions during the driving task. In addition, safety research has focused on eye tracking as a means of capturing driver's attention, fatigue, or drowsiness; however, the body posture has not been investigated in depth. This paper presents a novel approach for studying the actual movements of drivers inside the vehicle, when performing specific maneuver types such as lane changing and merging. A pilot study was conducted along a freeway and arterial segment, where the 3D shapes of selected participants were constructed with the use of Microsoft Kinect range camera while merging and changing lanes. A 7-point human skeletal model was fit to the captured range data (depth frame sequences) using the proposed framework. The analysis of the captured 3D data showed that there are important differences between participants when performing similar driving maneuvers. The preliminary results of this pilot research set the basis for implementing the proposed methodological framework for conducting full-scale experiments with a variety of participants, and exploring differences due to driver behavior attributes, such as age, gender and driving experience.
Kondyli et al. (Thu,) studied this question.