Towards In-vehicle Driver Fainting Detection

Current interior sensing systems already enable the detection of critical driver states such as drowsiness or inattention. In order to extend the system's capabilities, this work firstly investigates a possible detection of driver fainting via an interior sensing camera. An approach that supports the simulation of driver fainting is developed and realized in a parked vehicle as well as during manual and automated driving, with 61 participants in total. Moreover, multiple instructed intentional movements with for- and side-ward movements of the body are recorded. Classification models are developed based on features that are derived from head and body pose data. These models are then applied to the complete video streams that include various waiting and driving scenarios. The best classification results are seen with Random Forest classifiers with up to 84\% true positive detections and 0.33~false positive detections per hour. The majority of false positive detections were seen during automated driving. Implications and options for future research are discussed.