Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology
This addresses the critical testing and evaluation problem for CAV developers, offering a more efficient approach, though it appears incremental as it builds on existing optimization and scenario-based testing concepts.
The study tackles the lack of a systematic framework for generating testing scenario libraries for connected and automated vehicles (CAVs) by proposing a general methodology that uses scenario criticality and optimization to reduce the number of tests needed compared to on-road methods, with theoretical proof of accuracy.
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.