  This paper presents a white-box intention-aware decision-making for the
handling of interactions between a pedestrian and an automated vehicle (AV) in
an unsignalized street crossing scenario. Moreover, a design framework has been
developed, which enables automated parameterization of the decision-making.
This decision-making is designed in such a manner that it can understand
pedestrians in urban traffic and can react accordingly to their intentions.
That way, a human-like response to the actions of the pedestrian is ensured,
leading to a higher acceptance of AVs. The core notion of this paper is that
the intention prediction of the pedestrian to cross the street and
decision-making are divided into two subsystems. On the one hand, the intention
detection is a data-driven, black-box model. Thus, it can model the complex
behavior of the pedestrians. On the other hand, the decision-making is a
white-box model to ensure traceability and to enable a rapid verification and
validation of AVs. This white-box decision-making provides human-like behavior
and a guaranteed prevention of deadlocks. An additional benefit is that the
proposed decision-making requires low computational resources only enabling
real world usage. The automated parameterization uses a particle swarm
optimization and compares two different models of the pedestrian: The social
force model and the Markov decision process model. Consequently, a rapid design
of the decision-making is possible and different pedestrian behaviors can be
taken into account. The results reinforce the applicability of the proposed
intention-aware decision-making.
