Probabilistic driving style determination by means of a situation based analysis of the vehicle data

Today, driver assistance systems assist the driver in manifold ways. Their acceptance and usefulness can be highly increased by adapting them to the needs and the personality of the driver. In this work the driving style of the driver is determined by means of rating the drivers actions in commonly occurring traffic situations. Therefore, the vehicle data is evaluated and a probabilistic affiliation of the driver being aggressive, anxious, economical, keen, or sedate is made. The situations are chosen to be day-to-day traffic situations, for instance approaching a village, stopping on a stop sign, or passing through a tight bend. Based on the determined driving style, future driving assistance systems can be personalized to the individual driver and, thus, get more valuable. As a showcase, we adjust our Anticipatory Energy Saving Assistant (ANESA) to the drivers character, which is giving driving hints how to save energy in tight curves. By personalizing ANESA more credence is gained, resulting in extra savings of energy as we show in the experiments made.