Reliability, Resilience, and Alerts: Preferences for Autonomous Vehicles in the United States

Self-driving vehicle (SDV) safety and reliability are becoming critical design parameters as SDVs increase their market share. This paper examines public preferences for key SDV safety features (system reliability, sensor resilience, failure behavior, and driver alert methods) using a choice-based conjoint survey of 403 U.S. respondents. A novel integration of conjoint analysis with Least Absolute Shrinkage and Selection Operator (LASSO) regression and generalized linear mixed-effects models (GLMMs) was applied to identify the most influential features and their demographic or behavioral predictors. Results show that multimodal driver alerts (i.e., audio + visual) were the most influential factor, accounting for nearly two-thirds of decision weight. System reliability (i.e., low human intervention rates) and sensor resilience (i.e., low tolerance for failures) were secondary, while failure behavior had minimal influence. Subgroup analyses revealed modest variations by willingness to pay for SDVs, income, race/ethnicity, marital status, education, driving frequency, and risk propensity, though the importance of alerts and reliability remained consistent across groups. This combined conjoint-LASSO-GLMM framework enhances the precision of preference estimation and offers actionable guidance for SDV manufacturers seeking to align safety feature design with consumer expectations.