Predicting driver left-turn behavior from few training samples using a maximum a posteriori method

In this work, we introduce a novel maximum a posteriori (MAP) method, which can predict driver left-turn behavior from only a few training samples. For the prediction of the driver behavior in this scenario we utilize the so-called critical gap. It signifies how large a gap minimally has to be for the driver to accept it and take the turn. The latter is especially important for the personalization of an intersection assistant, which we are currently developing. In contrast to Troutbeck's critical gap estimation method, we define a likelihood over all observable accepted and rejected gaps, thus we provide a model that does not require the driver to behave consistently. Subsequently, we extend it to a MAP estimation by incorporating prior knowledge of the critical gap. Using this approach, we obtain a maximum prediction error of 13.8% if only one training sample is used, which is a relative improvement of 35.2% compared to Troutbeck's method.