A Novel Integrated Decision-making Evaluation Method Considering Individual Personalization Diversity

Abstract

Decision-making is one of the critical modules of autonomous driving, and how to evaluate its performance precisely remains a challenge. Although some current evaluation methods have considered multiple aspects of driving experience and been able to give a comprehensive evaluation result, the emphasis on individual personalization diversity is still limited. Existing methods mainly fit the evaluation model to an average human model, neglecting the individual demand features. In this paper, we propose a novel integrated decision-making evaluation method considering individual personalization diversity. First, we build an integrated evaluation model to represent the average human evaluation. Based on the four fundamental single-factor model including safety, time efficiency, comfort, and energy consumption, a segmental linear model is applied to combine various elements into one unified framework. Then a personalized weight fluctuation mechanism is proposed which adjusts the relative term weights in the integrated model dynamically according to the users’ preferences. Finally, a corresponding online individual demand distribution estimation method is designed to assess human personal diversity. The experiments on the D2E dataset prove that the proposed evaluation system can better adapt to individual preference diversity, reducing the evaluation mean absolute error by 7.40%. The higher the degree of personalization for the users, the greater the improvement this method can generate.