  Evaluating the safety of an autonomous vehicle (AV) depends on the behavior
of surrounding agents which can be heavily influenced by factors such as
environmental context and informally-defined driving etiquette. A key challenge
is in determining a minimum set of assumptions on what constitutes reasonable
foreseeable behaviors of other road users for the development of AV safety
models and techniques. In this paper, we propose a data-driven AV safety design
methodology that first learns ``reasonable'' behavioral assumptions from data,
and then synthesizes an AV safety concept using these learned behavioral
assumptions. We borrow techniques from control theory, namely high order
control barrier functions and Hamilton-Jacobi reachability, to provide
inductive bias to aid interpretability, verifiability, and tractability of our
approach. In our experiments, we learn an AV safety concept using
demonstrations collected from a highway traffic-weaving scenario, compare our
learned concept to existing baselines, and showcase its efficacy in evaluating
real-world driving logs.
