Shared spaces are urban areas without physical separation between motorised and non-motorised users. Previous research has suggested that it is difficult for users to appropriate these spaces and that the advent of self-driving cars could further complicate interactions. It is therefore important to study the perception of these spaces from the users’ perspectives to determine which conditions may promote their acceptance of the vehicles. This study investigates the perceived collision risk of a self-driving car’s passenger when pedestrians cross the vehicle’s path. The experiment was conducted with a driving simulator. Seven factors were manipulated to vary the dynamics of the crossing situations in order to analyse their influence on the passenger’s perception of collision risk. Two measures of perceived risk were obtained. A continuous subjective assessment, reflecting an explicit risk evaluation, was reported in real time by participants. On the other hand, their skin conductance responses, which reflects implicit information processing, were recorded. The relationship between the factors and the risk perception indicators was studied using Bayesian networks. The best Bayesian networks demonstrate that subjective collision risk assessments are primarily influenced by the factors that determine the relative positions of the vehicle and the pedestrian as well as the distance between them when they are in close proximity. The analysis further reveals that variations in skin conductance response indicators are more likely to be explained by variations in subjective assessments than by variations in the manipulated factors. These findings could benefit the development of self-driving navigation among pedestrians by improving understanding of the factors that influence passengers’ feelings.