Abstract

It is always uncertain if a new assistance system will enhance traffic safety or not: empirical studies indicate that driving style may deteriorate when the driver experiences the increased safety margin created by an advanced driver assistance system. To minimize this negative effect on driving style, we redesigned a night vision system so that it appeared differently to the driver: we let the system's head-up display be turned off during operation, to be lit up only when the system detected an obstacle (e.g., a pedestrian or animal) on the road ahead. This presentation style was compared in a simulator study to the traditional solution of constantly lit-up display. The results indicate that drivers reacted more reliably (showed less variance in reaction times) using the new system, which implies that the lighting up of the IR-display constituted an effective warning. Also, drivers to a greater extent drove at normal (slower) speeds when using the re-designed system. More generally, systems offering discontinuous support (i.e. only in critical situations) may have less of a negative effect on driving style, as their presence is not felt as vividly by the driver.

Abstract

Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts’ predictions of the future development. Document type: Article