Research on Application of Naive Bayes Algorithm Based on Attribute Correlation to Unmanned Driving Ethical Dilemma

Abstract

At present, unmanned driving technology has made great progress, while those research on its related ethical issues, laws, and traffic regulations are relatively lagging. In particular, it is still a problem how unmanned vehicles make a decision when they encounter ethical dilemmas where traffic collision is inevitable. So it must hinder the application and development of unmanned driving technology. Firstly, 1048575 survey data collected by Moral Machine online experiment platform is analyzed to calculate the prior probability that the straight being protector or sacrificer in ethical dilemmas with single feature. Then, 116 multifeature ethical dilemmas are designed and surveyed. The collected survey data are analyzed to determine decision-making for these ethical dilemmas by adopting the majority principle and to calculate correlation coefficient between attributes, then an improved Naive Bayes algorithm based on attribute correlation (ACNB) is established to solve the problem of unmanned driving decision in multifeature ethical dilemmas. Furthermore, these ethical dilemmas are used to test and verify traditional NB, ADOE, WADOE, CFWNB, and ACNB, respectively. According to the posterior probability that the straight being protector or sacrificer in those ethical dilemmas, classification and decision are made in these ethical dilemmas. Then, the decisions based on these algorithms are compared with human decisions to judge whether these decisions are right. The test results show that ACNB and CFWNB are more consistent with human decisions than other algorithms, and ACNB is more conductive to improve unmanned vehicle’s decision robustness than NB. Therefore, applying ACNB to unmanned vehicles has a good role, which will provide a new research point for unmanned driving ethical decision and a few references for formulating and updating traffic laws and regulations related to unmanned driving technology for traffic regulation authorities.