  The autonomous driving industry is expected to grow by over 20 times in the
coming decade and, thus, motivate researchers to delve into it. The primary
focus of their research is to ensure safety, comfort, and efficiency. An
autonomous vehicle has several modules responsible for one or more of the
aforementioned items. Among these modules, the trajectory planner plays a
pivotal role in the safety of the vehicle and the comfort of its passengers.
The module is also responsible for respecting kinematic constraints and any
applicable road constraints. In this paper, a novel online spatial-temporal
graph trajectory planner is introduced to generate safe and comfortable
trajectories. First, a spatial-temporal graph is constructed using the
autonomous vehicle, its surrounding vehicles, and virtual nodes along the road
with respect to the vehicle itself. Next, the graph is forwarded into a
sequential network to obtain the desired states. To support the planner, a
simple behavioral layer is also presented that determines kinematic constraints
for the planner. Furthermore, a novel potential function is also proposed to
train the network. Finally, the proposed planner is tested on three different
complex driving tasks, and the performance is compared with two frequently used
methods. The results show that the proposed planner generates safe and feasible
trajectories while achieving similar or longer distances in the forward
direction and comparable comfort ride.
