Classification of User Preference for Self-Driving Mode and Behaviors of Autonomous Vehicle

In the realm of semi-autonomous vehicles (semi-AVs), diverse facets of driver preferences in human-driver interaction have been explored. This study delves into the pivotal aspect of whether drivers favor a self-driving mode within semi-AVs. To discern such preferences, we conducted a user preference survey and leveraged the collected data to construct machine learning (ML) models capable of classifying these preferences effectively. Focusing on the prediction of preferred self-driving actions, we discerned user preferences at four granularity levels. At the lowest level, we ascertained whether users leaned towards self-driving mode at each traffic situation. For the highest granularity, we identified five distinct action types, each comprising two-staged actions (‘Act-Inform,’ ‘Inform-Act,’ ‘Act-No Inform,’ ‘Inform-Consent,’ ‘Alert-Handover’). Our online survey involved 85 participants from each age group (23-44 and 60+), who responded to a background questionnaire and situation-based inquiries for eighteen selected traffic situations. These situations were recorded in two regions of Ontario, Canada (Toronto and Waterloo), representing different population sizes and traffic conditions. Responses from the online survey were processed into features for ML models of user preference prediction. ML model optimization was achieved through Bayesian hyperparameter optimization and the Boruta SHAP (SHapley Additive exPlanations) feature selection algorithm. Boruta SHAP evaluated features and highlighted important features in each ML model of the two age group models (23-44 and 60+). Our findings underscore the feasibility of developing predictive models for driver preferences in self-driving behaviors, with average accuracies exceeding 85% and 72% at the lowest and highest granularity levels, respectively.