  Human drivers have limited and time-varying cognitive resources when making
decisions in real-world traffic scenarios, which often leads to unique and
stochastic behaviors that can not be explained by perfect rationality
assumption, a widely accepted premise in modeling driving behaviors that
presume drivers rationally make decisions to maximize their own rewards under
all circumstances. To explicitly address this disadvantage, this study presents
a novel driver behavior model that aims to capture the resource rationality and
stochasticity of the human driver's behaviors in realistic longitudinal driving
scenarios. The resource rationality principle can provide a theoretic framework
to better understand the human cognition processes by modeling human's internal
cognitive mechanisms as utility maximization subject to cognitive resource
limitations, which can be represented as finite and time-varying preview
horizons in the context of driving. An inverse resource rational-based
stochastic inverse reinforcement learning approach (IRR-SIRL) is proposed to
learn a distribution of the planning horizon and cost function of the human
driver with a given series of human demonstrations. A nonlinear model
predictive control (NMPC) with a time-varying horizon approach is used to
generate driver-specific trajectories by using the learned distributions of the
planning horizon and the cost function of the driver. The simulation
experiments are carried out using human demonstrations gathered from the
driver-in-the-loop driving simulator. The results reveal that the proposed
inverse resource rational-based stochastic driver model can address the
resource rationality and stochasticity of human driving behaviors in a variety
of realistic longitudinal driving scenarios.
