  The coordination of highly automated vehicles (or agents) in road
intersections is an inherently nonconvex and challenging problem. In this
paper, we propose a distributed motion planning scheme under reasonable
vehicle-to-vehicle communication requirements. Each agent solves a nonlinear
model predictive control problem in real time and transmits its planned
trajectory to other agents, which may have conflicting objectives. The problem
formulation is augmented with conditional constraints that enable the agents to
decide whether to wait at a stopping line, if safe crossing is not possible.
The involved nonconvex problems are solved very efficiently using the proximal
averaged Newton method for optimal control (PANOC). We demonstrate the
efficiency of the proposed approach in a realistic intersection crossing
scenario.
