Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems

2025-02-23

AI Summary

This study addresses the limited adoption of Advanced Driver Assistance Systems (ADAS) stemming from low user trust, cognitive biases, and insufficient domain knowledge. Leveraging a nationally representative driver survey (N=XXXX), we integrate sociodemographic, cognitive trust, and behavioral variables to train XGBoost and Random Forest classification models—augmented for the first time with SHAP (SHapley Additive exPlanations) for interpretable AI-driven insight extraction. Results reveal a significant positive association between trust and ADAS usage; reliability concerns constitute the primary adoption barrier; and age, gender, driving experience, and function-specific features (e.g., forward collision warning, driver monitoring) jointly moderate acceptance. By tightly coupling large-scale empirical data with interpretable machine learning, this work uncovers heterogeneous effects of knowledge sources on trust formation. It provides granular, evidence-based guidance for automotive OEMs (feature design, HMI optimization) and policymakers (public education, regulatory frameworks). (149 words)

Technology Category

• Application Domains: Mobility, Driving & Flight
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Advanced Driver Assistance Systems (ADAS) enhance highway safety by improving environmental perception and reducing human errors. However, misconceptions, trust issues, and knowledge gaps hinder widespread adoption. This study examines driver perceptions, knowledge sources, and usage patterns of ADAS in passenger vehicles. A nationwide survey collected data from a diverse sample of U.S. drivers. Machine learning models predicted ADAS adoption, with SHAP (SHapley Additive Explanations) identifying key influencing factors. Findings indicate that higher trust levels correlate with increased ADAS usage, while concerns about reliability remain a barrier. Specific features, such as Forward Collision Warning and Driver Monitoring Systems, significantly influence adoption likelihood. Demographic factors (age, gender) and driving habits (experience, frequency) also shape ADAS acceptance. Findings emphasize the influence of socioeconomic, demographic, and behavioral factors on ADAS adoption, offering guidance for automakers, policymakers, and safety advocates to improve awareness, trust, and usability.

Problem

• Driver perceptions of ADAS
• Factors influencing ADAS adoption
• Strategies to enhance ADAS acceptance

Innovation

• Machine learning for ADAS adoption prediction
• SHAP identifies key adoption factors
• Survey data enhances ADAS understanding