  Planning safe trajectories for autonomous vehicles in complex urban
environments is challenging since there are numerous semantic elements (such as
dynamic agents, traffic lights and speed limits) to consider. These semantic
elements may have different mathematical descriptions such as obstacle,
constraint and cost. It is non-trivial to tune the effects from different
combinations of semantic elements for a stable and generalizable behavior. In
this paper, we propose a novel unified spatio-temporal semantic corridor (SSC)
structure, which provides a level of abstraction for different types of
semantic elements. The SSC consists of a series of mutually connected
collision-free cubes with dynamical constraints posed by the semantic elements
in the spatio-temporal domain. The trajectory generation problem then boils
down to a general quadratic programming (QP) formulation. Thanks to the unified
SSC representation, our framework can generalize to any combination of semantic
elements. Moreover, our formulation provides a theoretical guarantee that the
entire trajectory is safe and constraint-satisfied, by using the convex hull
and hodograph properties of piecewise Bezier curve parameterization. We also
release the code of our method to accommodate benchmarking.
