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Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems

Hannah Musau, Nana Kankam Gyimah, Judith Mwakalonge, Gurcan Comert, Saidi Siuhi

TL;DR

The paper addresses drivers' willingness to adopt Advanced Driver Assistance Systems (ADAS) by combining a nationwide survey with an explainable AI framework. It deploys AutoGluon for automated predictive modeling, SHAP for transparent feature attribution, and GSDMM for open-ended response topic modeling to identify factors driving adoption. Key findings show that trust, prior exposure to ADAS, and financial considerations strongly influence adoption, while demographic effects are present but secondary; awareness levels and information sources also shape acceptance. The work offers actionable guidance for automakers and policymakers to improve awareness, trust, and usability, and suggests educational and adaptive strategies to maximize safety benefits.

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.

Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems

TL;DR

The paper addresses drivers' willingness to adopt Advanced Driver Assistance Systems (ADAS) by combining a nationwide survey with an explainable AI framework. It deploys AutoGluon for automated predictive modeling, SHAP for transparent feature attribution, and GSDMM for open-ended response topic modeling to identify factors driving adoption. Key findings show that trust, prior exposure to ADAS, and financial considerations strongly influence adoption, while demographic effects are present but secondary; awareness levels and information sources also shape acceptance. The work offers actionable guidance for automakers and policymakers to improve awareness, trust, and usability, and suggests educational and adaptive strategies to maximize safety benefits.

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.

Paper Structure

This paper contains 22 sections, 15 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methodology
  4. Data Collection
  5. Data Processing
  6. Predictive Modeling and Explainable AI (XAI) for ADAS Adoption
  7. AutoGluon for Automated Machine Learning (AutoML)
  8. SHAP for Model Interpretability
  9. Topic Modeling for Open-Ended Responses
  10. Experimental Setting
  11. Dataset
  12. Evaluation Metrics
  13. Results and Discussion
  14. Predictive Modeling and Explainable AI (XAI) for ADAS Adoption
  15. AutoGluon for Automated Machine Learning (AutoML)
  16. ...and 7 more sections

Figures (5)

  • Figure 1: The SHAP beeswarm plot illustrates feature contributions to ADAS acceptance predictions, with larger absolute SHAP values indicating greater influence. Colors represent feature values, from high (red) to low (blue).
  • Figure 2: Average rankings of factors considered in vehicle purchase decisions (lower ranks indicate higher importance).
  • Figure 3: Number of respondents aware of various ADAS features in vehicles equipped with ADAS.
  • Figure 4: Primary sources of ADAS information used by respondents.
  • Figure 5: Top twenty words in the top five topics extracted from open-ended responses.