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

arXiv: https://arxiv.org/abs/2202.08935 · HTML: https://mlsanity.com/paper/2202.08935/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2022.

## Built on by (most cited first, 2 of 2)

- [Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems](https://mlsanity.com/paper/2307.08939/) (2023)
- [Training Adversarial yet Safe Agent to Characterize Safety Performance of Highly Automated Vehicles](https://mlsanity.com/paper/2402.01576/) (2024)

## Abstract (arXiv)

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 εδ-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.