  Autonomous vehicles must balance a complex set of objectives. There is no
consensus on how they should do so, nor on a model for specifying a desired
driving behavior. We created a dataset to help address some of these questions
in a limited operating domain. The data consists of 92 traffic scenarios, with
multiple ways of traversing each scenario. Multiple annotators expressed their
preference between pairs of scenario traversals. We used the data to compare an
instance of a rulebook, carefully hand-crafted independently of the dataset,
with several interpretable machine learning models such as Bayesian networks,
decision trees, and logistic regression trained on the dataset. To compare
driving behavior, these models use scores indicating by how much different
scenario traversals violate each of 14 driving rules. The rules are
interpretable and designed by subject-matter experts. First, we found that
these rules were enough for these models to achieve a high classification
accuracy on the dataset. Second, we found that the rulebook provides high
interpretability without excessively sacrificing performance. Third, the data
pointed to possible improvements in the rulebook and the rules, and to
potential new rules. Fourth, we explored the interpretability vs performance
trade-off by also training non-interpretable models such as a random forest.
Finally, we make the dataset publicly available to encourage a discussion from
the wider community on behavior specification for AVs. Please find it at
github.com/bassam-motional/Reasonable-Crowd.
