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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

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Published on: December 18, 2020

Identifying interaction types and functionality for automated vehicle virtual assistants: An exploratory study using

Jediah R Clark¹, David R Large², Emily Shaw²

¹Agents, Interaction and Complexity (AIC) Research Group, Electronics & Computer Science, Building 32, Room 4001, University of Southampton, Southampton, SO17 1BJ, UK.

Applied Ergonomics

|October 19, 2023

Summary

This summary is machine-generated.

Virtual assistants in automated vehicles (AVs) use distinct interaction types for safe control transfers. Understanding these communication styles helps design better AI for drivers, ensuring situational awareness and a positive user experience.

Keywords:

Automated vehicles Autonomous vehicles Communication Interface design Natural language interfaces Virtual assistant

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Area of Science:

Human-Computer Interaction
Automated Vehicle Systems
Cognitive Psychology

Background:

Onboard virtual assistants are crucial for safe operation in automated vehicles (AVs).
Effective communication is vital for transferring control and responsibility in AVs.
Previous research emphasizes the need for situation information exchange during control transitions.

Purpose of the Study:

To explore and define 'interaction types' in human-machine communication during AV control transfer.
To analyze how situation information is conveyed and reciprocated between drivers and virtual assistants.
To provide a framework for designing future virtual assistants that enhance driver situation awareness and user experience.

Main Methods:

Utilized a driving simulator with dual controls for human-driver interaction studies.
Employed verbal communication exclusively for control transfer scenarios.
Coded handover dialogues using speech-act classifications and performed cluster analysis.

Main Results:

Identified four interaction types for virtual assistants (Supervisor, Information Desk, Interrogator, Converser).
Identified four interaction types for human drivers (Coordinator, Perceiver, Inquirer, Silent Receiver).
Established a framework characterizing each interaction type for driver requirements and virtual assistant design.

Conclusions:

The identified interaction types offer a framework for designing virtual assistants in AVs.
These designs can support drivers in maintaining and rebuilding situation awareness during control transfers.
Recommendations are provided for future virtual assistant designs, considering dialogue turns and takeover time for optimal user experience.