Biologically Inspired Automotive User Interfaces for Partially and Highly Automated Maneuver Gestures: Final Results and Outlook

Herzberger, Nicolas Daniel; Usai, Marcel; Preutenborbeck, Michael; Meyer, Ronald; Wessel, Gina; Flemisch, Frank · 2022 · Crossref

DOI: 10.54941/ahfe100975

archive: archived pipeline: cataloged verified

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Summary

This paper presents the final results of Project Vorreiter, which investigates biologically inspired automotive user interfaces for partially and highly automated driving. Motivated by the challenge of creating intuitive interactions between drivers and automation systems, the research utilizes the "H-Metaphor" (horse-rider analogy) to conceptualize control as a spectrum rather than a binary state. The core problem addressed is how drivers can intuitively initiate, supervise, and interrupt automated maneuvers through discrete gestures, specifically aiming for a universal design that accommodates novice and disabled drivers. The study developed two sets of steering wheel gestures: "Push/Twist" gestures involving force and torque, and "Swipe" gestures involving gentle touch or stroking. These were implemented on a capacitive steering wheel with integrated touch detection and LED visual feedback. The final evaluation was conducted in a high-fidelity driving simulator with 26 participants across eight driving scenarios. The experimental design compared manual control (baseline) against Push/Twist and Swipe gestures in SAE Level 2 (partially automated) and SAE Levels 3/4 (highly automated) modes. Participants underwent naive runs to assess intuitive comprehensibility and trained runs to evaluate performance, with data analyzed using signal detection theory to measure gesture recognition accuracy. The results indicated that over 75% of driving situations were successfully resolved regardless of the control method. Objective analysis revealed that most gesture inputs resulted in true positives or true negatives. However, SAE Level 2 systems exhibited specific difficulties: Swipe gestures suffered from a high rate of false positives due to the conflict between using the steering wheel for vehicle stabilization and gesture input. Push/Twist gestures in Level 2 faced issues with "oversteer," where gentle inputs failed to trigger automation, leading to unintended manual lane changes. Subjectively, participants preferred Push/Twist gestures over Swipe gestures. Notably, Push/Twist gestures in SAE Levels 3/4 were preferred over the manual baseline, suggesting higher automation levels mitigate the usability issues found in partial automation. The paper concludes that maneuver-based gesture controls are a viable alternative for future automated vehicles, particularly when paired with higher levels of automation. The findings highlight the need for design refinements to reduce misunderstandings between drivers and automation in partially automated modes. The authors emphasize the importance of standardizing these interaction concepts to prevent user complexity as automation technologies proliferate. Future work involves testing these concepts in real traffic using Wizard-of-Oz and theater vehicle setups to validate findings beyond static simulator environments.

Key finding

Twist/push steering gestures were preferred by participants and demonstrated superior performance compared to swipe gestures for maneuver initiation in automated driving scenarios.

Methodology

simulator

Sample size: 26

Provenance

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

Topics

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• automation
• control interfaces
• manual
• ehmi external hmi
• steering pattern
• automation surprise

Information type

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• Applied Guidance: design guidelines
• Theoretical Contribution: conceptual framework, computational model