  Generating safety-critical scenarios, which are essential yet difficult to
collect at scale, offers an effective method to evaluate the robustness of
autonomous vehicles (AVs). Existing methods focus on optimizing adversariality
while preserving the naturalness of scenarios, aiming to achieve a balance
through data-driven approaches. However, without an appropriate upper bound for
adversariality, the scenarios might exhibit excessive adversariality,
potentially leading to unavoidable collisions. In this paper, we introduce
FREA, a novel safety-critical scenarios generation method that incorporates the
Largest Feasible Region (LFR) of AV as guidance to ensure the reasonableness of
the adversarial scenarios. Concretely, FREA initially pre-calculates the LFR of
AV from offline datasets. Subsequently, it learns a reasonable adversarial
policy that controls critical background vehicles (CBVs) in the scene to
generate adversarial yet AV-feasible scenarios by maximizing a novel
feasibility-dependent objective function. Extensive experiments illustrate that
FREA can effectively generate safety-critical scenarios, yielding considerable
near-miss events while ensuring AV's feasibility. Generalization analysis also
confirms the robustness of FREA in AV testing across various surrogate AV
methods and traffic environments.
