In order to use technology to influence human behaviour and promote safer and more fuel efficient behaviour through incentive mechanisms, an instrumented vehicle is developed. The first step is to make it "perceive" the outside world, so extracting knowledge from some data sources such as sensors is crucial. More critically, there is a fundamental need for a standard that would enable knowledge sharing/exchanging among the different entities, e.g., between on-board sensors, invehicle controls and traffic management agencies. This paper proposes an Ontology for Context Modelling (OCM) to be used as the world model for driving context representation and reasoning, which can enable a better understanding of traffic context and sensor capability, which is the basis for providing data source to Advanced Driver Assistance Systems (ADAS), V2X (Vehicle-To-Everything) communications and even driving decision making within autonomous vehicles. Through the experiments, we evaluate the capability of the OCM to represent the driving context and the reasoning mechanism to compensate for sensor failures and recognize lane changing and overtaking events. This methodology has significant value for creating standards in autonomous and semi-Autonomous cars.