  The capability to follow a lead-vehicle and avoid rear-end collisions is one
of the most important functionalities for human drivers and various Advanced
Driver Assist Systems (ADAS). Existing safety performance justification of the
car-following systems either relies on simple concrete scenarios with biased
surrogate metrics or requires a significantly long driving distance for risk
observation and inference. In this paper, we propose a guaranteed unbiased and
sampling efficient scenario-based safety evaluation framework inspired by the
previous work on $\epsilon\delta$-almost safe set quantification. The proposal
characterizes the complete safety performance of the test subject in the
car-following regime. The performance of the proposed method is also
demonstrated in challenging cases including some widely adopted car-following
decision-making modules and the commercially available Openpilot driving stack
by CommaAI.
