  Vehicle trajectory planning is a key component for an autonomous driving
system. A practical system not only requires the component to compute a
feasible trajectory, but also a comfortable one given certain comfort metrics.
Nevertheless, computation efficiency is critical for the system to be deployed
as a commercial product. In this paper, we present a novel trajectory planning
algorithm based on nonlinear optimization. The algorithm computes a
kinematically feasible and comfort-optimal trajectory that achieves collision
avoidance with static obstacles. Furthermore, the algorithm is time efficient.
It generates an 6-second trajectory within 10 milliseconds on an Intel i7
machine or 20 milliseconds on an Nvidia Drive Orin platform.
