Learning driver preferences for freeway merging using multitask irl

Most automobile manufacturers today have invested heavily in the research and design of implementing autonomy in their cars. One important and challenging problem faced by a self-driven car on highways is merging into the highway from an acceleration ramp. Successful merging needs consideration of the behaviors of cars driving in the outermost highway lane which is adjacent to the merging lane, especially, the behaviors of those cars that would potentially become the leading or following car after a successful merge. We attempt to predict the motivation for the behaviors of those cars driving on the outermost highway lanes near the merging area hypothesizing that they perform a series of tasks each of which is driven by different motivations while passing through each section of the merging area. We use a Hierarchical Bayesian model to model the preferences in each task and the priors over those preferences.