  Trajectory planning in urban automated driving is challenging because of the
high uncertainty resulting from the unknown future motion of other traffic
participants. Robust approaches guarantee safety, but tend to result in overly
conservative motion planning. Hence, we propose to use Stochastic Model
Predictive Control for vehicle control in urban driving, allowing to
efficiently plan the vehicle trajectory, while maintaining the risk probability
sufficiently low. For motion optimization, we propose to use a two-stage
hierarchical structure that plans the trajectory and the maneuver separately. A
high-level layer takes advantage of a long prediction horizon and of an
abstract model to plan the optimal maneuver, and a lower level is in charge of
executing the selected maneuver by properly planning the vehicle's trajectory.
Numerical simulations are included, showing the potential of our proposal.
