  Many models account for the traffic flow of road users but few take the
details of local interactions into consideration and how they could deteriorate
into safety-critical situations. Building on the concept of sensorimotor
control, we develop a modeling framework applying the principles of utility
maximization, motor primitives, and intermittent action decisions to account
for the details of interactive behaviors among road users. The framework
connects these principles to the decision theory and is applied to determine
whether such an approach can reproduce the following phenomena: When two
pedestrians travel on crossing paths, (a) their interaction is sensitive to
initial asymmetries, and (b) based on which, they rapidly resolve collision
conflict by adapting their behaviors. When a pedestrian crosses the road while
facing an approaching car, (c) either road user yields to the other to resolve
their conflict, akin to the pedestrian interaction, and (d) the outcome reveals
a specific situational kinematics, associated with the nature of vehicle
acceleration. We show that these phenomena emerge naturally from our modeling
framework when the model can evolve its parameters as a consequence of the
situations. We believe that the modeling framework and phenomenon-centered
analysis offer promising tools to understand road user interactions. We
conclude with a discussion on how the model can be instrumental in studying the
safety-critical situations when including other variables in road-user
interactions.

Machine Learning