# Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios
## 6 Conclusions  
This study presents a fuzzy comprehensive evaluation method based on the Spherical Fuzzy Analytical Network Process (SF-ANP) to address the challenges of imperfect scenario evaluation and the difficulties in quantifying evaluation metrics in vehicle virtual testing. The method takes into account the interactions among various traffic elements in vehicle scenarios by modeling these relationships as a network. To evaluate scenario data, a five-level numerical interval system is used, which helps to classify new data and improves the selection of scenarios for specific tests. This approach enhances efficiency and allows for the classification of critical scenarios. As a simple and versatile template, all newly collected scenarios can be quantified in a uniform way, and a scenario library serving different testing needs can be built in a reasonable way. In addition, the evaluation metrics derived from this method can be used as a benchmark for designing high-risk and effective test scenarios, and effective critical scenarios can be generated in large quantities by learning the distribution of scenarios through the scenario characteristics of the quantitative metrics. The validation using Baidu Apollo's real-world crash data shows consistency between the proposed evaluation method and the simulation results.  
Future research can propose adaptive assessment methods that will be tied to the simulation results through feedback mechanisms to continuously improve the accuracy of the assessment. Models with different algorithms will be used to test quantitative scenarios and enhance the adaptability of the evaluation system through corrections. We will consider more different functional indicators, including comfort, economy, etc., to emphasize the all-round performance of the autonomous vehicles on the basis of ensuring safety. Future research will focus on refining the test scenario evaluation system and applying it to vehicle safety testing. We will use different datasets to improve the coverage of the scenario assessment and further validate the assessment metrics. Using evaluation as the scenario standard and test results as the assessment index of vehicle function will greatly promote the autonomous vehicles. Systematic and standardized testing will help in the realization of intelligent transportation.  
However, this study has some limitations. There are no established standards for evaluating testing scenarios, meaning that our scenario elements, based on current viewpoints, may need ongoing updates. The selected sub-elements may not fully capture the characteristics of the overall elements. As AV technology evolves, the evaluation criteria should expand beyond the current metrics. Also, the method used to quantify scenario risk and complexity relies on qualitative judgments, which could lead to errors. The scenarios used in this study are based on real-world crash data, but the number of scenarios is limited. To improve validation, it is recommended to expand the scenario library to include a broader range of real-world scenarios, such as natural driving data, to ensure comprehensive coverage.  
Conceptualization, Z.W. and R.Z.; methodology, Z.W.; software, Z.W.; validation, R.Z. and H.Z.; formal analysis, H.Z.; investigation, H.Z.; resources, R.Z.; data curation, R.Z.; writing--original draft preparation, Z.W.; writing--review and editing, R.Z. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of Hunan Province, China. No.2024 JJ7624.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical restrictions.  
The authors declare no conflicts of interest.