Comptes rendus de l’Acade'mie bulgare des Sciences, Vol 72, No5, pp.650-657

Ontology-based Personalisation for Online Driver Monitoring by Smartphone

Igor Lashkov, Alexey Kashevnik, Andrey Ronzhin


Recently, there has been a trend of rapid development of intelligent active safety systems for vehicle drivers. These systems are aimed at driver monitoring and online dangerous states detection. However, they are limited in adaptation and personalization techniques as well as with context utilization and generation of situation-relevant recommendations. Such recommendations are expected to be aimed at preventing traffic accidents or reducing the probability of their occurring. The paper proposes an ontology-based approach to online driverís monitoring by smartphone camera. Ontology provides the deployment of the driver and vehicle specific parameters that are assumed for the personalization support. Based on the driver the ontology contains driverís parameters such as eyes, mouth size, face orientation during the driving, etc. The ontology contains the information about driver, vehicle, context, dangerous situations and recommendations evaluated by the developed mobile application. The proposed ontology model will serve as a basis for the creation of dangerous situation prevention systems focused on monitoring driver behaviour.

Key words: ontology, smartphone, sensors, driver, driver behaviour, vehicle, dangerous state

DOI: 10.7546/CRABS.2019.05.13