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The capability to follow a lead-vehicle and avoid rear-end collisions is oneof the most important functionalities for human drivers and various AdvancedDriver Assist Systems (ADAS). Existing safety performance justification of thecar-following systems either relies on simple concrete scenarios with biasedsurrogate metrics or requires a significantly long driving distance for riskobservation and inference. In this paper, we propose a guaranteed unbiased andsampling efficient scenario-based safety evaluation framework inspired by theprevious work on $\epsilon\delta$-almost safe set quantification. The proposalcharacterizes the complete safety performance of the test subject in thecar-following regime. The performance of the proposed method is alsodemonstrated in challenging cases including some widely adopted car-followingdecision-making modules and the commercially available Openpilot driving stackby CommaAI.

A Formal Safety Characterization of Advanced Driver Assist Systems in the Car-Following Regime with Scenario-Sampling