User comfort and naturalness of automated driving: The effect of vehicle kinematics and proxemics on subjective response

Peng, Chen; Wei, Chongfeng; Solernou, Albert; Hagenzieker, Marjan; Merat, Natasha · 2023 · Crossref

DOI: 10.31219/osf.io/8hkb5

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study investigates how the kinematic and proxemic factors of Automated Vehicle (AV) driving styles influence user perceptions of comfort and naturalness, specifically within SAE Level 4+ automated driving contexts. As users in higher-level AVs act primarily as passengers, their subjective experience is critical for public acceptance. The research addresses a gap in understanding how specific vehicle metrics—such as acceleration, jerk, lateral offset, and yaw—impact these subjective evaluations, and whether "human-like" driving styles, defined by similarity to manual driving, enhance user experience. The researchers utilized a high-fidelity, motion-based driving simulator to conduct a within-participant experiment involving 24 licensed drivers. Participants experienced three distinct Level 4 automated driving styles: two human-like styles (defensive and aggressive, derived from representative human drivers) and one machine-learning-based style. These drives were conducted across 24 varied UK road sections categorized by speed limit and curvature. Participants rated their comfort and naturalness for each controller using 11-point Likert scales. To quantify driving styles, the study calculated maximum absolute values of kinematic (speed, acceleration, jerk) and proxemic (lateral offset) factors in longitudinal, lateral, and vertical/rotational directions. Additionally, the Euclidean distance between automated and manual driving metrics was computed to measure the objective similarity between the AV’s behavior and the participant’s own driving style. Mixed-effects models were employed to analyze the relationship between these objective vehicle metrics and subjective ratings. The results indicated that lateral and rotational kinematic factors had significant effects on both comfort and naturalness evaluations, whereas longitudinal kinematic factors played a less prominent role. Specifically, metrics such as lateral acceleration, yaw, and yaw rate were key determinants of user perception. Furthermore, the study found that greater similarity between automated and manual driving styles significantly enhanced user comfort and naturalness. This similarity was characterized by closer alignment in vehicle metrics including speed, longitudinal jerk, lateral offset, and yaw. The findings suggest that users perceive driving styles that closely mimic their own manual driving habits as more comfortable and natural. This research contributes to the design of user-centered AV controllers by identifying specific kinematic and proxemic factors that drive subjective user responses. By demonstrating that lateral and rotational dynamics are more critical to comfort than longitudinal ones, and that mimicking human driving patterns improves acceptance, the study provides actionable insights for AV developers. These findings support the development of driving algorithms that prioritize naturalness and comfort, thereby facilitating better public uptake of higher-level automated vehicles.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• passenger motion sickness comfort
• acceptance adoption

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: self report data
• Theoretical Contribution: computational model