ProVoice: Designing Proactive Functionality for In-Vehicle Conversational Assistants using Multi-Objective Bayesian Optimization to Enhance Driver Experience

TL;DR

This paper presents ProVoice, a VR-based study using Multi-Objective Bayesian Optimization to optimize proactive in-vehicle voice assistant interactions, improving driver experience by balancing mental demand, predictability, and usefulness.

Contribution

It introduces a novel application of MOBO in designing proactive IVCA interventions, demonstrating effective trade-offs and potential for broad scalability.

Findings

MOBO successfully identified optimal intervention strategies.

ProVoice reduced mental demand while increasing predictability and usefulness.

The approach offers a scalable framework for personalized IVCA design.

Abstract

The next step for In-vehicle Conversational Assistants (IVCAs) will be their capability to initiate and automate proactive system interactions throughout journeys. However, diverse drivers make it challenging to design voice interventions tailored towards individual on-road expectations. This paper evaluates the effectiveness of Human-in-the-Loop (HITL) Multi-Objective Bayesian Optimization (MOBO) in design by implementing ProVoice: a Virtual Reality (VR) driving simulator integrating MOBO to investigate the effects of IVCA design variants on perceived mental demand, predictability, and usefulness. By reporting the Pareto Front from a within-subjects VR study (N=19), this paper proposes optimal design trade-offs. Follow-up analysis demonstrates MOBO's success in discovering effective intervention strategies, with reduced participant mental demand, alongside enhanced predictability and usefulness while engaging with the proactive IVCA. Implications for computational techniques in future research on proactive intervention strategies are discussed. ProVoice can extend to include alternative design parameters and driving scenarios, encouraging intervention design on a broad scale.