# Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology

## TL;DR

This paper introduces a systematic framework for generating testing scenario libraries for connected and automated vehicles, enabling efficient evaluation across various operational domains with fewer tests.

## Contribution

It proposes a novel methodology for testing scenario library generation using criticality measures and optimization, with theoretical validation and plans for reinforcement learning enhancements.

## Key findings

- Framework achieves accurate evaluation with fewer tests
- Scenario criticality combines maneuver challenge and exposure
- Optimization method effectively identifies critical scenarios

## Abstract

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs), and yet there is no systematic framework to generate testing scenario library. This study aims to provide a general framework for the testing scenario library generation (TSLG) problem with different operational design domains (ODDs), CAV models, and performance metrics. Given an ODD, the testing scenario library is defined as a critical set of scenarios that can be used for CAV test. Each testing scenario is evaluated by a newly proposed measure, scenario criticality, which can be computed as a combination of maneuver challenge and exposure frequency. To search for critical scenarios, an auxiliary objective function is designed, and a multi-start optimization method along with seed-filling is applied. The proposed framework is theoretically proved to obtain accurate evaluation results with much fewer number of tests, if compared with the on-road test method. In part II of the study, three case studies are investigated to demonstrate the proposed methodologies. Reinforcement learning based technique is applied to enhance the searching method under high-dimensional scenarios.