We tackle the problem of distinguishing driver intentions in visual distractions to accomplish comfortable human-machine interactions. Our discrimination targets are threefold: no visual distractions, i.e., looking directly ahead, and two types of visual distractions that have opposite affects on safety, i.e., checking side blind spots and gazing at non-driving-related objects. The stochastic relationship between driver states and the three types of observations, or the driver's physical actions, artifact operations, and driving situations, is modeled with a Dynamic Bayesian Network. Our experiments with a realistic driving simulator demonstrated how effectively of the proposed method enabled these driver states to be recognized.
|Published - 2009
|16th World Congress on Intelligent Transport Systems and Services, ITS 2009 - Stockholm, Sweden
Duration: 2009 Sept 21 → 2009 Sept 25
|16th World Congress on Intelligent Transport Systems and Services, ITS 2009
|09/9/21 → 09/9/25