# Test case sampling optimization for safety validation of automated driving systems

## TL;DR

This paper introduces a method to select test cases for validating automated driving systems, ensuring they reflect real-world conditions and capture rare safety-critical scenarios.

## Contribution

The novel Kernel Test Case Sampling method balances representativeness and coverage for efficient safety validation of automated driving systems.

## Key findings

- The method captures long-tailed scenarios while approximating naturalistic driving conditions.
- It enables robust accident-rate estimation for fair comparisons between human drivers and automated systems.
- The approach supports standardized and scalable safety validation for automated driving systems.

## Abstract

Testing and validating automated driving systems require carefully designed test cases that capture the complexity of real-world driving conditions. However, the inherent complexity of driving environments and the rarity of safety-critical situations pose significant challenges to developing reliable and efficient validation frameworks. This paper addresses these issues by selecting appropriate test cases from the largest-scale naturalistic driving study. We introduce a Kernel Test Case Sampling method, which selects cases satisfying two key criteria: representativeness, ensuring alignment with real-world scenarios, and coverage, capturing high-risk corner cases. To demonstrate the proposed method, it is applied to large-scale naturalistic driving study data. By selecting a limited number of cases, the method effectively captures long-tailed scenarios while approximating the distribution of naturalistic driving conditions. The sampling framework also enables robust accident-rate estimation, thereby ensuring fair comparisons across human driving performance and multiple systems. The proposed method supports standardized and scalable automated driving system safety validation, facilitating accelerated development and deployment while building public trust and regulatory confidence.

This work introduces a method for selecting test cases from large-scale naturalistic driving studies to validate automated driving systems. It balances representativeness and coverage using a kernel-based approach, enabling fair comparisons with human drivers and supporting efficient safety validation.