  Understanding human driving behavior is important for autonomous vehicles. In
this paper, we propose an interpretable human behavior model in interactive
driving scenarios based on the cumulative prospect theory (CPT). As a
non-expected utility theory, CPT can well explain some systematically biased or
``irrational'' behavior/decisions of human that cannot be explained by the
expected utility theory. Hence, the goal of this work is to formulate the human
drivers' behavior generation model with CPT so that some ``irrational''
behavior or decisions of human can be better captured and predicted. Towards
such a goal, we first develop a CPT-driven decision-making model focusing on
driving scenarios with two interacting agents. A hierarchical learning
algorithm is proposed afterward to learn the utility function, the value
function, and the decision weighting function in the CPT model. A case study
for roundabout merging is also provided as verification. With real driving
data, the prediction performances of three different models are compared: a
predefined model based on time-to-collision (TTC), a learning-based model based
on neural networks, and the proposed CPT-based model. The results show that the
proposed model outperforms the TTC model and achieves similar performance as
the learning-based model with much less training data and better
interpretability.
