  One of the challenges to reduce the gap between the machine and the human
level driving is how to endow the system with the learning capacity to deal
with the coupled complexity of environments, intentions, and dynamics. In this
paper, we propose a hierarchical driving model with explicit model of
continuous intention and continuous dynamics, which decouples the complexity in
the observation-to-action reasoning in the human driving data. Specifically,
the continuous intention module takes the route planning map obtained by GPS
and IMU, perception from a RGB camera and LiDAR as input to generate a
potential map encoded with obstacles and intentions being expressed as grid
based potentials. Then, the potential map is regarded as a condition, together
with the current dynamics, to generate a continuous trajectory as output by a
continuous function approximator network, whose derivatives can be used for
supervision without additional parameters. Finally, we validate our method on
both datasets and simulator, demonstrating that our method has higher
prediction accuracy of displacement and velocity and generates smoother
trajectories. The method is also deployed on the real vehicle with loop
latency, validating its effectiveness. To the best of our knowledge, this is
the first work to produce the driving trajectory using a continuous function
approximator network.
