  We propose a new scheme to learn motion planning constraints from human
driving trajectories. Behavioral and motion planning are the key components in
an autonomous driving system. The behavioral planning is responsible for
high-level decision making required to follow traffic rules and interact with
other road participants. The motion planner role is to generate feasible, safe
trajectories for a self-driving vehicle to follow. The trajectories are
generated through an optimization scheme to optimize a cost function based on
metrics related to smoothness, movability, and comfort, and subject to a set of
constraints derived from the planned behavior, safety considerations, and
feasibility. A common practice is to manually design the cost function and
constraints. Recent work has investigated learning the cost function from human
driving demonstrations. While effective, the practical application of such
approaches is still questionable in autonomous driving. In contrast, this paper
focuses on learning driving constraints, which can be used as an add-on module
to existing autonomous driving solutions. To learn the constraint, the planning
problem is formulated as a constrained Markov Decision Process, whose elements
are assumed to be known except the constraints. The constraints are then
learned by learning the distribution of expert trajectories and estimating the
probability of optimal trajectories belonging to the learned distribution. The
proposed scheme is evaluated using NGSIM dataset, yielding less than 1\%
collision rate and out of road maneuvers when the learned constraints is used
in an optimization-based motion planner.
