Multidimensional Evaluation of Autonomous Driving Test Scenarios Based on AHP-EWN-TOPSIS Models

Abstract The advancement of autonomous vehicles (AVs) requires robust evaluation methods to ensure both safety and efficiency. To incorporate multiple dimensions in designing test scenarios, this paper proposes a multidimensional evaluation framework for AV test scenarios based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. The evaluation considers three dimensions: risk, complexity, and rarity. First, the test scenario is deconstructed into its constituent elements. Then, the weights of these elements are determined from both subjective and objective perspectives using the Analytic Hierarchy Process (AHP) and Entropy Weight Method. Then, game theory is employed to optimize these weights, deriving the optimal balance between subjective and objective weights. Next, three different scenario libraries are utilized as case studies, and a comprehensive evaluation index is calculated using the TOPSIS model. Subsequently, the scenarios are categorized into four levels using K-means clustering algorithm. Finally, the accuracy and reliability of the framework are verified through simulation. The simulation results demonstrate the effectiveness of the framework in identifying critical scenarios and providing valuable insights for AV testing.