  The motion planners used in self-driving vehicles need to generate
trajectories that are safe, comfortable, and obey the traffic rules. This is
usually achieved by two modules: behavior planner, which handles high-level
decisions and produces a coarse trajectory, and trajectory planner that
generates a smooth, feasible trajectory for the duration of the planning
horizon. These planners, however, are typically developed separately, and
changes in the behavior planner might affect the trajectory planner in
unexpected ways. Furthermore, the final trajectory outputted by the trajectory
planner might differ significantly from the one generated by the behavior
planner, as they do not share the same objective. In this paper, we propose a
jointly learnable behavior and trajectory planner. Unlike most existing
learnable motion planners that address either only behavior planning, or use an
uninterpretable neural network to represent the entire logic from sensors to
driving commands, our approach features an interpretable cost function on top
of perception, prediction and vehicle dynamics, and a joint learning algorithm
that learns a shared cost function employed by our behavior and trajectory
components. Experiments on real-world self-driving data demonstrate that
jointly learned planner performs significantly better in terms of both
similarity to human driving and other safety metrics, compared to baselines
that do not adopt joint behavior and trajectory learning.
