Towards a cognitive warning system for safer hybrid traffic

Technological development brings increasingly closer the era of widely available self-driving cars. However, presumably there will be a time where human drivers and self-driving cars would share the same roads. In the current paper, we propose a cognitive warning system that utilizes information collected from the behaviour of the human driver and sends warning signals to self-driving cars in case of human related emergency. We demonstrate that such risk detection can identify danger earlier than an external sensor would, based on the behaviour of the human-driven vehicle. We used data from a simulator experiment, where 21 participants slalomed between road humps in a virtual reality environment. Occasionally, they had to react to dangerous roadside stimuli by large steering movements. We used one-class SVM to detect emergency behaviour in both steering and vehicle trajectory data. We found earlier detection of emergency based on steering wheel data, than based on vehicle trajectory data. We conclude that tracking cognitive variables of the human driver means that we can utilize the outstanding power of the brain to evaluate external stimuli. Information about the result of this evaluation (be it steering action or saccade) could be the basis of a warning signal that is readily understood by the computer of a self-driving car.