Scenario Complexity Evaluation for Open-Road Testing of Intelligent Connected Vehicles

Open-road testing is essential for intelligent connected vehicles (ICVs), yet mileage alone does not capture the difficulty of the scenarios encountered by automated and assisted driving functions. This study proposes a field-oriented scenario-complexity evaluation method for ICV open-road testing. A scenario-element library is constructed from standards, accident-investigation experience, system failure modes, and field-test observations. Environmental, road, and traffic-flow factors are integrated into a weighted complexity index. The case analysis uses recorded scenario conditions, perception outputs, vehicle-function states, and driver interventions. The field-test campaign covered more than 10,000 km in the Pearl River Delta, from which twelve illumination-related trial records were selected for the case analysis presented in this paper. These trials illustrate variations in the complexity scores and observed functional degradation across three illumination-related scenario families. The results can support the selection of demanding test scenarios and the organization of scenario libraries, with complexity scores interpreted alongside functional outcomes rather than as estimates of accident risk.