  There is quickly growing literature on machine-learned models that predict
human driving trajectories in road traffic. These models focus their learning
on low-dimensional error metrics, for example average distance between
model-generated and observed trajectories. Such metrics permit relative
comparison of models, but do not provide clearly interpretable information on
how close to human behavior the models actually come, for example in terms of
higher-level behavior phenomena that are known to be present in human driving.
We study highway driving as an example scenario, and introduce metrics to
quantitatively demonstrate the presence, in a naturalistic dataset, of two
familiar behavioral phenomena: (1) The kinematics-dependent contest, between
on-highway and on-ramp vehicles, of who passes the merging point first. (2)
Courtesy lane changes away from the outermost lane, to leave space for a
merging vehicle. Applying the exact same metrics to the output of a
state-of-the-art machine-learned model, we show that the model is capable of
reproducing the former phenomenon, but not the latter. We argue that this type
of behavioral analysis provides information that is not available from
conventional model-fitting metrics, and that it may be useful to analyze (and
possibly fit) models also based on these types of behavioral criteria.
