<p>With the increasing development of self-driving vehicles, there are various substantial risks in the interaction between automated driving technology and conventional transport system along with users, how to prioritize the risks involved in self-driving vehicles is regarded as a considerably complex multi-criteria decision making (MCDM) problem. In response to this, this study aims to propose a novel hybrid MCDM method for quantitatively identifying and prioritizing major types of risks related to self-driving vehicles. The interval type-2 hesitant fuzzy linguistic term set is used to express double uncertainties on the mutual influence degrees among criterion performances associated with each alternative, and the decision information and date are acquired with the help of experienced decision makers (DMs). Following this, a cumulative prospect theory modified PROMETHEE II model considering risk preference is developed to prioritize risks in self-driving vehicles. The findings of this study indicate that the cyber attack risk (A2), reputational risk (A1) and internet outage risk (A3) are specified as the top three prioritized risks with the integrated prospect values of 1.215, 0.213, and − 0.136, respectively. The sensitivity analysis illustrates that the final prioritization of risks is influenced by changing criterion weight, attributes of criteria, and DMs’ risk preferences. The comparative analysis demonstrates that the proposed model turns out to be practicable and feasible due to its large distinction degree.</p>