Inspired on natural selective attention studies, we propose a computational model of selective attention that relies on the assumption that uncertain, surprising and motive congruent/incongruent information demands attention from an intelligent agent. This computational model has been integrated into the architecture of a Belief-Desire-Intention artificial agent so that this can autonomously select relevant, interesting information of the (external or internal) environment while ignoring other less relevant information. The advantage is that the agent can communicate only that interesting, selective information to its processing resources (focus of the senses, decision-making, etc.) or to its human owner's processing resources so that these resources can be allocated more effectively. We illustrate and provide experimental results of this role of the artificial, selective attention mechanism in the time-critical, risky situation, of driving a vehicle, by showing that it prevents both the personal traffic assistant agent's and its human owner's decision-making resources of receiving unnecessary traffic information.