  Vehicle-pedestrian interaction (VPI) is one of the most challenging tasks for
automated driving systems. The design of driving strategies for such systems
usually starts with verifying VPI in simulation. This work proposed an improved
framework for the study of VPI in uncontrolled pedestrian crossing scenarios.
The framework admits the mutual effect between the pedestrian and the vehicle.
A multi-state social force based pedestrian motion model was designed to
describe the microscopic motion of the pedestrian crossing behavior. The
pedestrian model considers major interaction factors such as the accepted gap
of the pedestrian's decision on when to start crossing, the desired speed of
the pedestrian, and the effect of the vehicle on the pedestrian while the
pedestrian is crossing the road. Vehicle driving strategies focus on the
longitudinal motion control, for which the feedback obstacle avoidance control
and the model predictive control were tested and compared in the framework. The
simulation results verified that the proposed framework can generate a variety
of VPI scenarios, consisting of either the pedestrian yielding to the vehicle
or the vehicle yielding to the pedestrian. The framework can be easily extended
to apply different approaches to the VPI problems.

Robotics