  Driving in a human-like manner is important for an autonomous vehicle to be a
smart and predictable traffic participant. To achieve this goal, parameters of
the motion planning module should be carefully tuned, which needs great effort
and expert knowledge. In this study, a method of learning cost parameters of a
motion planner from naturalistic driving data is proposed. The learning is
achieved by encouraging the selected trajectory to approximate the human
driving trajectory under the same traffic situation. The employed motion
planner follows a widely accepted methodology that first samples candidate
trajectories in the trajectory space, then select the one with minimal cost as
the planned trajectory. Moreover, in addition to traditional factors such as
comfort, efficiency and safety, the cost function is proposed to incorporate
incentive of behavior decision like a human driver, so that both lane change
decision and motion planning are coupled into one framework. Two types of lane
incentive cost -- heuristic and learning based -- are proposed and implemented.
To verify the validity of the proposed method, a data set is developed by using
the naturalistic trajectory data of human drivers collected on the motorways in
Beijing, containing samples of lane changes to the left and right lanes, and
car followings. Experiments are conducted with respect to both lane change
decision and motion planning, and promising results are achieved.
