Empirical Validation of Human-Centered Driving Style Parameterization in Highly Automated Vehicles

Kurzfassung

Trust and acceptance of highly automated vehicles (HAVs) are strongly influenced by how automated driving behavior aligns with user expectations and perceived driving styles. Prior work has shown that users can meaningfully interact with parameterized driving styles for automated vehicles and converge on stable preferences when supported by intuitive human-machine interfaces (HMIs) (Forster et al., 2019, Bellem et al., 2016). However, it remains unclear whether these preferences correspond to users’ natural manual driving behavior at the level of executed vehicle dynamics. This paper builds on earlier work (Trende et al., 2019) that defined semantic automated driving styles for highway scenarios by presenting a validation study and a behavioral comparison between manual and automated driving. Fourteen participants completed a driving simulator experiment consisting of a combination of manual and automated driving sessions in which they adjusted driving style parameters using a graphical HMI. Objective vehicle performance data were recorded in both conditions and driving style features capturing speed, smoothness, lane positioning and time headway were extracted. Clustering analysis revealed distinct driving style groups for both manual and automated driving. Participant wise similarity analysis, however, showed that preferred automated driving behavior often differed from participants’ manual driving behavior. Automated driving was consistently characterized by lower speeds, smoother acceleration, more centered lane positioning and larger following distances. These findings indicate that while users can converge on stable automated driving style preferences, such preferences do not necessarily reflect imitation of their own driving behavior. Instead, users appear to favor automated behavior that emphasizes comfort and perceived safety. The results highlight the importance of combining predefined driving style presets with flexible personalization mechanisms when designing user-centered automated driving systems.