Inside the Driver’s Mind: Mapping Customers’ Usage Behavior of Advanced Driving Assistance Systems

No ratings

Presented at HICSS 2025 by

Timo Böttcher

Christian Micus

Gouthaman Ramalingam

Benedikt Steiner

This study addresses the critical gap in understanding advanced driving assistance systems (ADAS) usage by exploring driving behavior profiles. We use a Bayesian Gaussian Mixture Model to analyze a substantial dataset of 232,849 drives from 55,864 vehicles in January 2022. Our results unveil six distinct vehicle usage profiles, shedding light on the correlations between individual ADAS usage and influencing factors such as customer driving behavior, mobility patterns, and the environmental and vehicle context. Furthermore, we map these profiles to Roger’s innovation adaptation groups, providing valuable insights into adoption dynamics. The findings contribute to classifying diverse car usage profiles, enabling the identification and prioritization of cluster-specific needs. Theoretical implications arise from the nuanced understanding of ADAS usage changes, contributing to the theoretical understanding of user adoption behaviors and preferences. These insights present new opportunities for personalizing functions and interactions to meet the evolving needs of distinct driver profiles.