  Trajectory planning for autonomous driving is challenging because the unknown
future motion of traffic participants must be accounted for, yielding large
uncertainty. Stochastic Model Predictive Control (SMPC)-based planners provide
non-conservative planning, but do not rule out a (small) probability of
collision. We propose a control scheme that yields an efficient trajectory
based on SMPC when the traffic scenario allows, still avoiding that the vehicle
causes collisions with traffic participants if the latter move according to the
prediction assumptions. If some traffic participant does not behave as
anticipated, no safety guarantee can be given. Then, our approach yields a
trajectory which minimizes the probability of collision, using Constraint
Violation Probability Minimization techniques. Our algorithm can also be
adapted to minimize the anticipated harm caused by a collision. We provide a
thorough discussion of the benefits of our novel control scheme and compare it
to a previous approach through numerical simulations from the CommonRoad
database.
